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Comparison of liver parenchyma textural features from
B-mode ultrasound with Fibroscan® results
Dulce Isabel Marcolino Gadelha
Thesis to obtain the Master of Science Degree in
Biomedical Technologies
Supervisor: Professor Ricardo Miguel da Silva Teresa Ribeiro
Professor João Miguel Raposo Sanches
Examination Committee
Chairperson: Professor Raul Daniel Lavado Carneiro Martins
Supervisor: Professor Ricardo Miguel da Silva Teresa Ribeiro
Members of the committee: Professor Dr. Rui Tato Marinho
June, 2014
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iii
Acknowledgements
Over the last few months, I had the privilege to spend this time with people who helped
me on a personal level and learning.
First of all I would like to thank Professor Ricardo Ribeiro and João Sanches, my
supervisors. Professor João Sanches, gave me the opportunity of entering in an area slightly
different from my licentiate degree formation, and organized the necessary steps, to the thesis
process. To Ricardo Ribeiro, I would like to express my gratitude because he gave me
suggestions and supporting during this research. The guidance and wise words made this work
possible. To Professor Rui Tato Marinho, for making possible access of this study to hepatic
ultrasound images, from cases that were already being followed by the Hospital de Santa Maria.
To all my friends, I would like to thank for enriching my life, providing moments that
allowed the resumption of the work in a more balanced way. In these people, I mention Maria
Bandeira and Dora Bernardes, friends who always accompanied me in this stage, and with
whom I have shared all the anguishes and happiness that can arise over a thesis work. I also
thank their help on a review of the work. My special thanks go to Filipe Brigues that constantly
inspired me to conquer my goals and gave me wonderful moments. He has been an especially
great help, and an important piece, always keeping me in touch, through his encouraging
words, with the importance of continue the work.
To my parents, João José Gadelha and Adelina Maria Marcolino Gadelha, and
remaining family I thank all patience, and unconditional love, supporting me and my decisions.
My brother, João Marcolino Gadelha, tried to give me strength to continue despite the
difficulties. Without their support, and friendly shoulder in all occasions, nothing of what I
accomplished so far would be possible. For this reason, I will be eternally grateful.
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Abstract
The main goal of this work was interpret the texture classification results, comparing the
Fibroscan® results, in order to understand if there are differences depending on the analyzed
image location. It will be verified the difference between the features usefulness in classification
and staging fibrosis when features obtained from three groups of ROIs.
Proven the Fibroscan® effectiveness in fibrosis quantification, we proposed a study
method, highly affordable, of the ultrasound image characteristics that may, in the future, be
added to this reference technique, using the same ultrasound standards. The ultrasound signal
recovered reflects directly the architecture of structures, so is expected that the modified
texture, by the different pathologies involved, demonstrate these changes.
This work performs a statistical data treatment, based on textural features obtained from
62 ultrasound images through the GLCM process. It was applied a texture analysis to the
information derived from 13 patients (Male and Female) with different fibrosis levels, especially
low levels. Comparing the Fibroscan® results and the results from the ModelTesting based on
central ROIs were registered shifts between 0 and 0,79 kPa. Right and Left ROIs revealed the
influence of position selection in diagnostic. The ROC analysis showed the applicability of this
method based on central features with a value of AUROC of 0,914. Positive and Negative
Predictive Values presented percentages of 100 and 88,9, respectively. Sensitivity and
specificity parameters demonstrated 80% and 100%, respectively and the Overall accuracy
revealed 92,3%.
Keywords: Ultrasound, Features, Texture analysis, Fibrosis, Fibroscan®
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Resumo
O grande objectivo deste trabalho é interpretar os resultados de classificação textural,
comparando os resultados do Fibroscan®, de modo a perceber se existem diferenças
significativas, consoante os locais de uma imagem analisados. Verificar-se-á a diferença de
utilidade das características na classificação de estadiamento da fibrose, quando obtidas
características de três grupos de ROIs.
Comprovada a eficácia do Fibroscan® na quantificação de fibrose, propôs-se um
método de estudo, de características da imagem ecográfica que futuramente poderá ser
adicionado a esta técnica de referência, utilizando os mesmos padrões de ecografia. O sinal do
ultrassom recuperado reflecte directamente a arquitectura das estruturas, sendo expectável
que a textura alterada, pelas diferentes patologias em causa demonstre essas alterações.
Este trabalho realiza um tratamento de dados estatísticos, baseados nas
características texturais recolhidas de 62 imagens ecográficas através do processo de GLCM.
Foi aplicada uma análise de textura à informação derivada de 13 doentes (Masculino e
Feminino) com diferentes níveis de fibrose, mas especialmente níveis baixos. Comparando os
resultados provenientes do Teste ao Modelo baseado nos ROIs centrais e os do Fibroscan®,
foram registados desvios entre 0 e 0,79 KPa. Os ROIs à direita e à esquerda revelaram a
influência da posição de selecção no diagnóstico. A análise ROC mostrou a aplicabilidade
deste método através das características centrais com AUROC de 0,914. Os valores de PPV e
NPV apresentam percentagens de 100 e 88,9, respectivamente. Os parâmetros de
Sensibilidade e a Especificidade demonstraram 80% e 100%, respectivamente e a Precisão
revelou 92,3%.
Palavras-Chave: Ultrassom, Características, Análise Textural, Fibrose, Fibroscan®
vi
Contents
Acknowledgements iii
Abstract iv
Resumo v
Contents vi
List of Tables viii
List of Figures ix
Acronyms x
Notation xi
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
1.1 Aim and objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Motivation and contributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
1.3 Features in texture analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Thesis structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2. Literature review. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
2.1 Chronic liver disease (CLD). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Chronic liver disease classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Methods applied in evaluation of hepatic fibrosis. . . . . . . . . . . . . . . . . . . . . . . . . . .14
2.2.1 Liver Biopsy (invasive method) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14
2.2.2 Non-invasive methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
2.2.2.1 Methods by medical imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 15
2.2.2.2 Other modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .18
2.2.2.3 Fibroscan® . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19
3. Ultrasound characterization by image texture analysis. . . . . . . . . . . . . . . . . . . . . . . . . . .23
3.1 Texture analysis approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Description statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.1 US Features. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
vii
3.2.2 Influence of Region-Of-Interest depth on image texture. . . . . . . . . . . . . 28
4. Procedure and experimental results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.1.1 Stepwise regression analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
4.1.2 Principal components analysis (PCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32
4.1.3 Data fitting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.1.4 Receiver operating characteristics (ROC). . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Procedure according to the methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.3.1 Results collection of the features. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42
4.3.2 Statistical results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.4 Discussion of results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
4.4.1 Surface fitting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45
4.4.1.1 Comparison of Fibroscan® results with textural features results . . . . . . .53
4.4.2 ROC analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57
5. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66
Bibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
viii
List of Tables
1 Liver fibrosis stages according to METAVIR score, with diagnostic options or treatment.
2 Comparison of the characteristics image modalities.
3 Patients information.
4 Different combinations of degrees (up to 4) for the polynomial model fitting, the results of
goodness of fit and representative functions of each model.
5 Ideal results of goodness-of-fit for an excellent data representation by surface fitting.
6 Goodness-of-fit of poly41 in central and right ROIs.
7 All values of goodness of fit for different types of ROI position applied in a poly41 model,
and the ideal values to obtain.
8 Fibroscan® results vs. right features, left features and central features (shifts of Fibroscan®
results).
9 Mean values for the positions of divided ROIs and all ROIs on the XY axis.
10 Assessment to ROC curve performance.
11 Expected characteristics for different threshold types.
12 Minimum distance (maximum correct classification) between ROC curve and the point (0,
1).
13 Possible results for diagnostic test.
14 Results obtained from ROC analysis (central model).
15 Diagnostic performance of transient elastography for significant fibrosis in chronic hepatitis
B and C.
A. 1
16 Description of features extracted from US images.
A. 2
17 Average of extracted features from the selected ROIs in different images and texture of this
area of ultrasound, only for the first patient, as well as the spatial relationship of the first
pixel.
A. 3
18 The model assessment and results obtained with PCA for X and Y features, wherein the P1,
P2, P3… P13 are the patients studied.
ix
List of Figures
1 - Percentage of cases that lead to transplant in hepatic diseases.
2 - Number of imaging exams in the world.
3 - Steps involved in pattern recognition systems.
4 - Concordance between liver stiffness and different fibrosis stages represented by: F0/F1 – 1,
F1/F2 – 2, F2 – 3, F2/F3 – 4, F3 – 5, F3/F4 – 6 and F4 – 7.
5 - Proposed feature extraction method to textural features study.
6 – Possible results of diagnostic test in ROC curves.
7 - Example of different ROI selection (52x52 pixels) by ImageJ software.
8 - Brief layout of the procedure followed in this thesis.
9 - CROP saved from an ultrasound image.
10 - Representative scheme of the average coordinates in the three groups of selected ROIs.
11 – Best surface fitting for central ROIs (model poly41).
12 - Residuals plot for central ROIs (model poly41).
13 - Residuals plot for right ROIs (model poly41).
14 - Residuals plot for left ROIs (model poly41).
15 - Residual Plots (residual values vs. fitted values) for central, right and left ROIs groups.
16 - ROC curve for central ROIs.
17 - Characteristics resulting from ROC analysis and the cutoffs created.
x
Acronyms
CLD – Chronic Liver Disease
ROI – Region-Of-Interest
GLCM – Gray Level Co-Occurrence Matrix
PCA – Principal Components Analysis
DFE – Degree of Freedom in the Error.
SSE – Sum of Squares as a result of Error
RMSE – Root Mean Squared Error
ROC – Receiver Operating Characteristics
AUROC – Area Under Receiver Operating Characteristics
TPR – True Positive Rate
FPR – False Positive Rate
TNR – True Negative Rate
FNR – False Negative Rate
PPV – Positive Predictive Value
NPV – Negative Predictive Value
OA – Overall Accuracy
PLR – Positive Likelihood Ratio
NLR – Negative Likelihood Ratio
SE – Standard Error
ci – confidence interval
co – Cutoff points
xi
Notation
σ : Stress applied to the material
ε : Strain induced in the material
E : Stiffness value
ρ : material density
Vs : shear velocity
(Δx, Δy) : distance between two pixels
Ө : direction angle
I and j : intensities
y : data
𝑦 : fit
d : distances between the ROC curve and the point (0, 1)
1
59
1410 8 6
0,8 0,8 0,08
Cirrhosis Cancers Cholestatic
diseases
Acute
hepatic
failure
Metabolic
diseases
Benign liver
tumors or
Polycystic
disease
Budd Chiar Parasitic
diseases
1. Introduction
Nowadays, is vital to develop an accessible and non-invasive method that has the ability to
discriminate early stages in METAVIR score, so that the liver does not get to irreversible states of
fibrosis.
The ultrasound scan is the most common exam, which is used to get this kind of information
from liver. However, at different locations it could not be as useful as intended. This is an important
factor, because in diffuse diseases, the lesion local is unknown.
This work will examine the extension of significant differences in classification of fibrosis
staging between different regions of ultrasound, comparing them with Fibroscan® results.
Recently the knowledge and treatment of liver disease has increased, since 29 million people
in the European Union still suffers with a chronic liver disease. The incidence and prevalence of
cirrhosis and primary liver cancer are fundamental to understanding the burden of liver disease. They
represent the end-stage of liver pathology and thus are indicative of the associated mortality. The
statistics show that 170,000 deaths per year occur due to CLD and there are large differences
between countries [1].
The International Agency for Research on Cancer reveals that the incidence of all types of
cancer is about 7,9% in men and 6,5% in women. For liver cancer is shown that, this type of cancer
has an incidence similar to mortality. Variations in the age, sex, and race rates of Hepatocellular
Carcinoma rates in different geographic regions are likely related to the prevalence of hepatitis viruses
in the populations, as well as the timing of the spread of the viral infection and the age of individuals at
the time of the infection [2].
.
Figure 1 - Percentage of cases that lead to transplant in hepatic diseases, adapted from [1].
2
As observed in the percentages graphic (Figure 1), there is a high number of cirrhosis cases
that may decrease, if previous diagnosis of fibrosis were made.
Different pathologies can lead to chronic liver disease. Patients with a cirrhosis stage have a
high risk for liver cancer. Because of this, will be described pathologies that lead to decrease of liver
function.
Viral hepatitis: inflammation of the liver caused by one of virus forms. The hepatitis A is an
infection usually caused by consuming contaminated food or water. This form of hepatitis
usually clears without lasting problems within six months and does not lead to a chronic
infection. Hepatitis B is transmitted through bodily fluids that leads to liver damage, but just a
small number of these cases can develop into a chronic infection. The form of hepatitis C
appears through contact with infected blood and may remain in the liver for years causing
injure [3].
Fatty liver disease: most common in diabetic or metabolic syndrome patients. Excess liver
fat can lead to inflammation (Nonalcoholic Steatohepatitis, NASH), progressing to scarring or
cirrhosis in 20 percent of patients [3].
Autoimmune liver disease: can occur in cases that the body’s immune system attacks the
liver [3].
Hepatocellular carcinoma: Patients with certain liver diseases are susceptible to liver cancer
[3].
Some conditions might reveal an accelerated development of cirrhosis, possibly occurring
within months rather than years such as neonatal liver disease, HCV infected patients after liver
transplantation, patients with HIV/HCV co-infection, severe delta hepatitis and some cases of drug
induced liver disease. These examples can reflect defective immunity, massive inflammation and
necrosis, and/or altered matrix resorption [4].
Liver functions become engaged when the organ develops high fibrosis levels, losing the
normal microscopic lobular architecture with fibrosis and nodular regeneration, leading to resistance to
blood flow through the liver [5].The liver becomes inflamed leading complications of CLD such as
portal hypertension, variceal bleeding, ascites and hepatic encephalopathy. Complications of chronic
liver disease can be markers for decompensated liver disease and which can turn in a diagnosis of
cirrhosis. When this unstable form of liver disease is transformed into cirrhosis, is important to
evaluate the patient for his survival, surgical outcomes and risk of complications [6].
The diagnosis of this kind of diseases can be accomplished by different methods such as the
clinical examination, blood markers, Fibroscan®, imaging (US, MRI and endoscopy), hepatic biopsy
and biological work up. All these tests have their advantages and disadvantages, defined by low
sampling little prediction values staging or danger to the patient, then an ideal test to diagnosis liver
fibrosis should not be invasive and simultaneously it would show characteristics such as simplicity,
reproducibility, readily available, less expensive than biopsy, predicts full spectrum of fibrosis and
reflects changes occurring along the therapy [7].
3
0
50
100
150
200
250
300
Ultrasound CT MRI Nuclear
Exam
s (
millio
ns)
Imaging exams in 2000
The Figure 2 shows the impact in the world of the exams performed using ultrasound. As we
can observe, ultrasound is by far the more accomplished exam type. Because of this, it would be
important develop reliable diagnostic methods based on ultrasound.
Liver biopsy can yield false-negative results in one-third of cases and is characterized by
morbidity rate of 3%, according to data from survey. So, noninvasive methods become urgent,
including ultrasonographic techniques that have been proposed and tested for detection of liver
fibrosis. All radiological modalities like ultrasound, CT, Scintigraphy, SPECT, MR, PET, X-rays, and
others, play an important role in process of disease diagnosing and have become major evidence to
ensure disease. However, US benefits of a rare combination of advantages including portability,
invasive, real-time data acquisition and affordability [9].
In order to explore the relationship between fibrosis classifications from Fibroscan® and report
of fibrosis based on ultrasound images was stored information about image texture (features), by
Gray-Level Co-occurrence Matrix. Texture features can help in a classification based on tonal features
such as mean, variance, skewness and kurtosis of gray levels along with texture features computes
from gray level co-occurrence matrices in analyzing images. Medical applications in image analysis
perform extraction of features from the image in order to distinguishing pathological and healthy
tissue, being capable to capture morphological properties or certain textural properties of the image
[10]. This properties offers the possibility of visualize subcutaneous body structures and internal
organs for possible pathology or lesions. As the histological alterations may bring about texture
changes in the US image, is possible obtain a quantification through texture analysis, being applied to
the classification of pathological tissues from the liver [11].
Texture analysis is applied in US images to obtain a routine diagnostic practice that involves
an ensemble of mathematical computations performed with the data contained within the images.
Some methods are employed to evaluate the inter-relationships of the pixels, the forms of texture
analyses are categorized as model-based methods and statistical approaches [11].
Figure 2 - Number of imaging exams in the world, adapted from [8].
4
There are many types of texture features such as Gray-Level Histogram, Run-Length matrix,
Wavelet transforms, Gray-Level Co-occurrence Matrix, etc. However, in this study was used GLCM
that can measures the texture through the distribution of intensities, but not about the relative position
of pixels with respect to each other in that texture. Using a statistical approach such as co-occurrence
matrix will help to provide valuable information about the relative position of the neighboring pixels in
an image [11].
Concluding, this work aims to verify the difference between the usefulness of the features in
classification and staging fibrosis when texture information is obtained from different locals from a liver
ultrasound. Will be interpreted the results of texture classification through a comparison with the
Fibroscan® results, in order to realize if differ significantly, depending of the locations analyzed.
Firstly, will be described the main objectives of the present study in order to understand the
motivation that led to its realization. The features are the basic elements in this study and will be
discussed why and how it is useful. Finally, it is summarized the thesis structure with the intention of
realize the work body that will be spoken later.
1.1 Aim and objectives
This study proposes understand the clinical value of features from US patterns in the
classification and staging of Chronic Liver Disease. More specifically, this will be perceived based on a
comparison of features obtained from ultrasound with stage values of chronic liver diseases collected
by the Fibroscan®. To perform this comparison, is observed the discrepancy between the fibrosis
classifications based on ultrasound and the Fibroscan® results. Because of this, it will be studied with
more accuracy, the possible difference of utility in diagnostic based on features derived from different
areas of the ultrasound image. In other words, as is not under consideration focal fibrosis, we intend to
understand if deviations of the ROI selections (coordinates) represent significant differences in the
diagnosis. It is expected that the most central zone of the ultrasound image, due to proximity to the
transducer (less noise), represents more faithfully the patient fibrosis level. However, the ideal
scenario would be check that the surrounding regions also represent some utility for diagnostics.
This work intends to verify the significant differences between different data types, especially
when is mentioned the depth factor of the waves propagated in liver from ultrasound. The depth factor
is considered relevant because as it increases, the noise often arises in ultrasound image, which may
hinder the process of getting the intended data.
Nowadays there are already interesting results in the classification of the liver fibrosis stage by
non-invasive methods such as the meaning of textural analysis of regions of interest of the liver
ultrasound image. In image classification the goal is to classify different images or image regions into
distinct groups [12]. However, there are no comparisons performed between different locations in the
US and Fibroscan®.
5
Ribeiro et al. (2013) [13] previously held a work that aimed to develop:
―a classification framework for diagnosis and staging of chronic liver disease (CLD) based on
clinical, laboratory and ultrasound data. In this context was analyzed the characteristics of steatosis
and cirrhosis, and if there could be a correlation between MELD (Model for End stage Liver Disease)
score and ultrasound information‖ [13].It was shown that the features of selected regions of the
ultrasounds can be different with the change of the selection position. The aim is to continue studying
the clinical potential of texture analysis of ultrasound images by selecting ROIs with different positions
in the images axis. With this, we intend to understand the influence of these changes in the
classification of the disease stage.
1.2 Motivation and contributions
The liver is the largest internal organ of the body and performs many functions, more than
5000 separate bodily functions. This including synthesis of largely serum proteins, regulation of
glucose and lipids, and production of bile. In other words, clean the blood of toxins to converting food
into nutrients to controlling your hormone levels. These days, liver disease is rising as a result of virus,
damage from drugs or chemicals, obesity, diabetes or an attack from your own immune system. As is
an organ responsible for various functions, the fact of presenting lesions, may be prejudicial to the
patients [3].Many habitual liver diseases can cause the organ to become inflamed, and that can
progress to scarring, or cirrhosis. There are a number of common diseases of the liver such as viral
hepatitis, fatty liver diseases, genetic liver diseases, autoimmune liver diseases, celiac disease and
liver cancer [3]. These conditions lead to a frequent cause of morbidity and mortality. Firstly, a few
symptoms characteristic of liver failure could appear, but they can be confused with symptoms of other
diseases, and this can cover the fact that liver is falling.
However, when the symptoms become more severe, the patient starts to get disoriented, and
extremely sleepy with possible risk of coma or death. To prevent aggressive risks, immediate
treatment is needed or liver transplant. Cirrhosis become visible when the liver has been failing
gradually for some time (CLD), so the development of a method that classifies early stages of fibrosis
(2 and 3 in METAVIR score) in a reliable way is very important, and with that the chance to heal and
recover the patient can be possible. Conditions like cirrhosis, liver cancer and liver failure can threaten
human life because once we have reached these stages of liver disease, the treatment possibilities
may be very limited [14].
Fibrosis can be measured noninvasively, based on liver stiffness that corresponds to a
genuine and intrinsic physical property of liver parenchyma or serum biomarkers that indicate several,
not strictly liver-specific features of blood, being associated to fibrosis stage, as assessed by liver
biopsy. Serum biomarkers in fibrosis measurement are characterized by high applicability and
6
potential widespread availability, being better than Fibroscan®. However, a critical interpretation of
results is required and it can be subjective in the presence/combination of two or more diseases in the
same patient. Despite the lower applicability, the Fibroscan® measurements reveal short procedure
time, immediate results, and the ability to perform the test at the bedside or in an outpatient clinic and
it is not a difficult procedure to learn. Other image methods are represented by Acoustic Radiation
Force Impulse imaging where the values in contrast to Fibroscan® values, have a narrow range, and
the Magnetic Resonance elastography that cannot be performed in livers of patients with iron
overload, due to signal noise limitations, costly and time-consuming in routine [15, 16].
Ultrasound studies reveal that this exam is a proven and useful procedure for the assessment
of many structures within abdomen areas [17]. Liver ultrasound is used as screening imaging in
patients suspicious for diffuse liver disease and is helpful in the term of follow-up examinations [15].
Analysis of diffuse liver disease assess echo texture, ultrasound attenuation, vascular architecture,
etc. as well as its surface, detecting more details of the superficially located structures of liver
parenchyma. This way, diagnosis of early cirrhotic stages and in the differential diagnosis of diffuse
parenchymal diseases, that are characterized through global transforms of the liver tissue, so that the
entire area of the liver is affected. As hepatic steatosis is common liver pathology, sensitivity and
specificity of the detection of hepatic steatosis by B-mode ultrasound examination may be very high.
Supporting findings may be ultrasound attenuation, that decreases detail analysis of vascular
architecture, and it may cause a loss of visibility deeper within the liver and impeded imaging of the
diaphragm. In liver cirrhosis, the ultrasound shows inhomogeneous echotexture, irregular-nodular liver
surface delineation and a variety of other possible findings including destroyed vascular architecture
also dependent on the etiology of diseases. Ultrasound is the first and most important imaging method
in suspected liver disease, which holds true both in the sense of proving and excluding pathology,
being an indispensable tool in clinical hepatology [18].
Using the advantageous technique of ultrasound, it is possible to obtain images where image
analysis techniques have an important role in several medical applications and involve the extraction
of features from the image, which are then used for a variety of classification tasks. Depending upon
the particular classification task, the extracted features capture morphological properties, color
properties, or certain textural properties of the image [10]. In diffuse liver diseases, will probably
appear modifications of the liver tissue and of the ultrasound properties of the tissue. To realize this,
texture-based analysis is considered necessary for establishing a correct diagnostic through non-
invasive methods [19]. Image texture of medical images describes internal structure of human tissues
or organs and pathological changes. The texture analysis of ultrasound images relies on that, if
disease processes affect the structure of the tissue, the tissue should reflect an altered ultrasound
signal, producing texture feature values different of the normal tissue [20]. Gray Level Co-occurrence
Matrix (GLCM) is based on texture methods and the corresponding second order statistics offer a
suitable method in order to do a statistical characterization of the gray level distributions in the case of
the liver textural pattern [10].
A study [21] reveals that liver textural characterization and surface morphology are the most
efficient US features in CLD diagnosis. Nevertheless, visual inspection and analysis of these patterns
7
is difficult and can cause errors in diagnosis and dependent operator. Giger et al. (2008) [22], proved
that limitations in the human eye-brain visual system, reader fatigue, distraction, and the presence of
overlapping structures that camouflage disease in images may cause detection and interpretation
errors. The interpretation of the ultrasound image with the human eye is always subjective because
this interpretation has high dependence on the ability and experience of the observer. This increases
the issue of accuracy and reproducibility [20].
Standard medical practice of soft tissue palpation is based on the qualitative assessment of
stiffness at low frequencies, such as the Fibroscan® that measures quite effectively the liver stiffness
in patients suffering from different chronic liver diseases [23]. It is generally known that pathological
changes derived from liver are correlated with changes in tissue stiffness. In diffuse diseases such as
chronic liver diseases, is verified a significant increase in tissue stiffness, also occurring in a
conventional US examination [24]. As is known, liver stiffness appears through fibrotic processes
along time, which will gradually changing the liver structure. These changes are scanned in the
Fibroscan® measurements as well as in the simple ultrasound images, due to changes in acoustic
impedance that reflect the stiffness difference of the tissue. Because of this, is expected that
definitions such as stiffness, texture and acoustic impedance of tissues presents some kind of direct
relationship in US.As the texture of a medical image can reflect the internal structure of an organ, is
expectable, the possibility to collect information from this texture, revealing the hepatic staging as
effectively as Fibroscan® reveals. In other words, is expected to find a relationship between stiffness
and textural changes, which seems possible to occur when presented this information. To try to
correlate these points will be used methods of texture analysis in images. Will be obtained
characteristic information, from its texture in order to posterior process of data and describe the
conclusions from this comparison study.
1.3 Features in texture analysis
This topic forms the basis of the present study because the features will be obtained with the
intention ofcharacterize the liver fibrosis of each patient, as previously mentioned. To understand the
image we intend to obtain a table of specific image properties (features) and after can be refined by
studying these properties of many samples [25].
Textural features contain information about the spatial distribution of gray variations within a
band. When a small-area (ROI) patch has a large variation of features of discrete gray tone, the
dominant property of that area is the texture. Like color, texture is an essential feature to consider
when querying image data bases. Texture occurs over a region, and is defined by gray levels, having
qualities such as periodicity and scale, described in terms of direction, coarseness, contrast and so
8
on. This makes texture an interesting facet of images and results in a multiplicity of ways of extracting
texture features [25].
Texture analysis methods provide unique information on the texture, or spatial variation of
pixels, of the region where they are applied. It is applied texture analysis methods to different image
regions and determined the precise location where texture feature values change significantly [12].
The texture of an image is represented by repeated patterns called visual texture. Image
texture is defined as a function of the spatial variation in pixel intensities called gray values and
applied in the recognition of image regions using texture properties. These properties (features) are
computed from the statistical distribution of observed combinations of intensities at specified positions
relative to each other in the image. According to the number of intensity pixels in each combination,
statistics are classified into first-order, second-order and higher-order statistics. In this specific work,
pretends extract second order statistical texture features by Gray Level Co-occurrence Matrix (GLCM)
method. This method is used for a long time to classify textures [20, 25]. GLCM is a method quite
used to understand different questions of liver ultrasound, so studies like Aggarwal et al., 2013
(Ultrasound Image Analysis of Cirrhosis Liver Disease Using SVM Classifier) [26], Huang et al., 2007
(Feature statistic analysis of ultrasound images of liver cancer) [27], Gao et al., 2013 (Texture analysis
and classification of ultrasound liver images) [28] works with this method, obtaining successful results
in this work phase.
Features are often extracted from raw data, ultrasound images in this case, and used to
represent it as sample patterns through matrix, which is then used to train and evaluate the classifier
itself during a learning process [29]. Patterns of features represent a repetition that crosses different
images or ROIs [30]. Referring feature patterns, is important understand that this term represent a set
of concepts where the elements are similar to one another in certain aspects, being possible describe
certain quantities, qualities, traits, notable features and so on [31]. Pattern recognition aims to make
the process of learning and detection of patterns explicit, such that it can partially or entirely be
implemented on computers [32]. Pattern recognition system has a mission to classify an object into a
correct class based on the measurements about the object [33].
In this study will be applied spatial patterns in the medical images, because the relationship
between pixels is verified spatially. To complete a process of pattern recognition, are required several
steps, in order to finally get the best classification, characterized by discrimination power. This process
begins with the data acquisition (measurements), where features are quantified, being dependent on
factors such as resolution, sensitivity, distortion, signal-noise ratio, etc. Measurements refer to some
observation about the object. The next steps are represented by techniques of feature selection or
pre-processing and feature extraction, where is removed the noise from data, isolating the patterns of
interest (minimum of features sufficient to classify the pattern) by Stepwise Regression Analysis and
find a new representation in terms of features by Principal Components Analysis. The result of the
feature extraction stage is called a feature vector which retains the most of the information that the
data vector contains. Following, are introduced the statistical model selection steps that domain
dependence and prior information and training that transmit pattern class definition into the system,
often, by showing a few typical examples of the pattern. Learning from a set of examples (training set)
9
Measurement
Pre-processing
Feature extraction
Classification
Post-processing
is a desired characteristic of most pattern recognition systems. In the classification (testing), the
trained classifier assigns the input pattern (feature vector) to one of the pattern classes under
consideration based on the measured features. The final task of the pattern recognition system is to
decide upon an action based on the classification results [31, 32, 33, 34, 35]. The following scheme
(Figure 3) shows in a quite summarized way the steps involved in a pattern recognition system.
The last statistical analysis procedures are initiated with a polynomial surface fitting technique.
Fitting models consists in find a mathematical function (polynomial in this study) which represents a
statistical variable already processed. With this method we can choice the best model/function,
estimate parameters and evaluate the quality of fit by the values obtained from goodness of fit
statistical tests [36]. Specifying the approximating function a mathematical form, is obtained
appropriate fitting routine then derives for the coefficients the values which provide the best fit of
chosen form [37].
Completed the steps mentioned, is discussed the accuracy of texture classification. In another
words, is intended classify the normal and abnormal tissue in the regions selected. [38].
1.4 Thesis structure
This study is divided into four principal parts. First we intend to select ROIs in different
positions. Secondly will be extracted the features from liver ultrasounds. The third part intends to
process the data in order to compare them to the Fibroscan® results and observe the associated error
verifying factors that may influence the results. The final stage, studies the diagnostic test referring its
strong and weaker points.
The third part analyzes the factors that may potentially influence the image texture (position of
ROI), which will in turn affect the accuracy of the texture analysis techniques and assessment of B-
Figure 3 - Steps involved in pattern recognition systems, adapted from [34].
10
mode liver images. In last phase, will be compared the best ROI set with Fibroscan® results, verifying
the error associated at this comparison.
Starting by describe the fundamental concepts in this work as well as others not applied but
important to mention, is included a chapter of Literature review. Where will be addressed all issues
related to chronic liver disease, important for the present study (CLD definition, staging classification
of disease, ultrasound-based diagnosis alternative techniques used in its diagnosis and Fibroscan®
accuracy).
Revealing methods used in texture analysis as well the influence factors in extracted features,
will be introduced a point where the ultrasound characterization by image texture analysis will be
described. In texture analysis process is present a method for data acquisition called Gray-Level Co-
occurrence Matrix, having as following methods responsible for data processing, in order to employ it
in the desired classification, which will also be studied.
When explained all essential concepts, the experimental results are presented, providing a
methodology followed by the work and comparison of final results, discussing the classification
accuracy.
Finally, is present the indispensable chapter of conclusion that provides some critical points of
results in this study and possible future works.
11
2. Literature review
In this chapter, are considered topics that mention the progression of liver injury, and
conditions that can expose a patient to chronic liver disease. Will also be discussed, diagnosis
hypotheses to this chronic disease, both invasive and non-invasive way, focused on the usefulness of
ultrasound and Fibroscan®.
2.1 Chronic Liver Disease (CLD)
Chronic liver disease accounts for a significant portion of the current health burden. Taking
information from previously registered data about this disease, the viral hepatitis, particularly hepatitis
C, then this is set to rise significantly over the coming decades. The increased consumption of alcohol
in young age, particularly young women, and the established epidemic of obesity in the western world,
cause a higher incidence of alcoholic liver disease and non-alcoholic fatty liver disease. With this
increase, the number of patients requiring both hospital and intensive care admission will
consequently rise and demand substantial increases in health care budgets [39].
This concern is once again mentioned how valuable is to develop non-invasive techniques,
that can guarantee characteristics like reproducibility and affordability.
Liver diseases, responsible for the most morbidity and mortality, are predominantly
represented by hepatitis, alcoholic liver disease, non-alcoholic fatty liver disease, cirrhosis and
hepatocellular carcinoma (HCC). These pathologies account for more than 95% of all deaths due to
liver disease [40]. The explosive growth in obesity worldwide has led to a dramatic increase in the
prevalence of non-alcoholic steatohepatitis (NASH), which also confers a risk of HCC once cirrhosis
develops [41].
Chronic liver disease has a significant weight in the morbidity and mortality in developed
nations. The consistency of diagnostic testing for CLD to identify asymptomatic patients in a high risk
population is important due to recent advances in management and treatment options, in other words,
better and precocious diagnosis, reveal more possibilities for the patient, choosing from several
options to control or treatment of disease. When clinicians often do not recognize the presence of
fundamental liver disease, precise documentation of the disorder can be delayed, leading to a
subsequent delay in an initiation of effective therapies. In turn, this identification provides better patient
outcomes if the diagnosis of fibrosis or cirrhosis can be made before cirrhosis become clinically
apparent [21]. Liver injuries can be divided broadly into patterns characterized by the occurrence
possibility of cholestatic or obstructive bile duct injury and hepatocellular or liver cell injury [42].
The liver lobule is constituted by two hepatic epithelial cell populations, called hepatocytes and
cholangiocytes, as well as by cells that are collectively defined as non parenchymal cells. Analyses
indicate that hepatocytes occupy almost 80% of the total liver volume, performing the majority of liver
functions [43]. Liver fibrosis results from accumulation of tough, fibrous scar tissue in the liver. This
scar formation is a usual bodily response to injury, but when the healing process goes wrong is formed
fibrosis. When hepatocytes are wounded due to a virus infection, excessive consumption of alcohol,
12
toxins, trauma or other factors, the immune system is activated to damage repair. The injury or
necrosis of hepatocytes stimulates inflammatory immune cells to liberate cytokines, growth factors,
and other chemicals that will support cells in the liver (hepatic stellate cells) to produce collagen,
glycoproteins, proteoglycans, and other substances. These substances cause the formation of
extracellular matrix (nonfunctional connective tissue) [44].
As the amount of fibrosis increases it can lead to disruptions in the normal shape and function
of the liver, decreasing hepatic capacity to metabolize and synthesize proteins, peptides and
hormones. The liver is responsible to filter blood that returns to the heart from the digestive system, so
when cirrhosis is present, the scar tissue causes increased resistance to blood flow through the liver.
These pressures developing in the veins that drain into the liver, a process called portal hypertension.
Patients with cirrhosis eventually die from complications such as spontaneous bacterial peritonitis,
variceal bleeding, liver failure or hepatocellular carcinoma [45].
Now, focusing approaches related to mechanics surrounding in the process of fibrosis, or
increases in liver stiffness (LS), fundamental concept for Liver Stiffness Measurement (LSM) applied
in Fibroscan®. As already mentioned, Fibroscan® (based on liver stiffness) is referred because their
classification results will be used in the experimental work, later on, it also be specified some of the
features of this diagnostic technique. Stiffness can be defined by the modulus of elasticity or Young’s
modulus (E).In this context is relevant talk about elasticity that can be defined by Hooke's law,
approximating states of extension of a material is directly proportional to the applied stress [46]. This
relationship is represented by:
σ = Eε (1)
Where σ is the stress applied to the material, and ε is the strain induced in the material. Stiffness value
is taken from E (kPa) and represents the resistance of material to deformation. Any biological soft
tissues should exhibit large strain even at low stress. This softness depends on four factors described
in the following points [46].
Extracellular matrix of the organ: deformable structure that transfers the external forces
through the liver.
Constraints: when is applied more pressure at boundaries of the liver, the stiffer it gets.
Internal pressure of the organ: liquid or specifically blood can come in and out, so the stiffness
will depend on the resistance that the organ applies to the movement of flow.
Viscous effects: influence the time constant over which stiffness is tested, in other words,
stiffness depends on frequency. The liver tends to be more stiffness ate high frequencies
(kilohertz) [46].
13
2.1.1 Chronic Liver Disease classification
To transform a system in an effective diagnostic practice in routine day, it must be simple to
understand and apply, communicate effectively to the treating clinician, and clinically relevant.
However, we should be careful about some systems that is most appropriate for clinical practice but
may not be the most informative for investigative work [47]. Hereupon, is necessary develop a system
that support both qualities (appropriate for clinical practice and accuracy information).
Many systems for scoring liver fibrosis aims, classify the progression of fibrosis to cirrhosis
into discrete stages, each based on visual assessment of collagen staining of liver biopsy samples.
Some systems for fibrosis staging are the histology activity index (HAI), the Ishak modification of the
HAI score and the METAVIR score [4].
The METAVIR score is the system used in this work and represent a semi-quantitative
classifications system and scores both necroinflammatory changes. The activity score is divided
according to the intensity of necroinflammatory lesions. The fibrosis score is assessed on a five point
scale, like we managed observes in the Table 1 [4].
Fibrosis development does not seem to be linear, that is, the immune system compromise, for
example due to co-infection with HIV also has been shown to accelerate fibrosis. Heavy alcohol
consumption is strongly associated with worsening fibrosis and cirrhosis. Finally, studies indicate that
steatosis (fatty liver) and insulin resistance are associated with more rapid and severe fibrosis.
Chronic infection with hepatitis C or hepatitis B virus (HCV or HBV) can lead to long-term liver damage
including fibrosis, cirrhosis, and HCC [41].
CLD has stages with different characteristics wherein the initial stage or the clinically
significant fibrosis is generally defined by F2, that is typically steatosis or hepatitis and the end-stage
(F3 or F4) of any liver disease is cirrhosis which usually empowers the development of hepatocellular
carcinoma. CLD involves progressive and chronic destruction and regeneration of liver parenchyma
(fatty liver) leading to cirrhosis, HCC and death. This malignant tumor is the most frequent in the
world, being the third cause of death from cancer in men [48].
Liver stiffness is a crucial factor in this score, because it is focused on staging by Fibroscan®,
being through this characteristic that can classify a fibrosis. According to METAVIR score, exist liver
stiffness cutoffs in CLD that allow us to divide into stages these diseases, being represented as
threshold values are defined in the Table 1 [7].
Table 1 - Liver fibrosis stages according to METAVIR score, with diagnostic options or treatment [7].
Metavir Liver stiffness (kPa) Description
F0-F1 2,5 -7 No fibrosis/fibrous portal expansion (no biopsy)
F2 7 - 9,5 Few bridges or septa (implementation of noninvasive tests)
F3 9,5 – 12,5 Numerous bridges
(implementation of noninvasive tests)
F4 12,5 - …75 Cirrhosis (Treatment or follow-up)
14
The METAVIR system has the advantage of simplicity, reproducibility and application to a
large number of fibrosis classifications [7].
2.2 Methods applied in evaluation of hepatic fibrosis.
Detection and quantification of liver fibrosis represents a challenge in Hepatology. Precise
evaluation of liver fibrosis has become important in order to make therapeutic decisions, determine
prognosis and to follow-up disease progression. Accurate and easily applied methods are for that
reason required for the assessment of hepatic fibrosis [49].
2.2.1 Liver Biopsy (invasive method)
There are different approaches for obtain liver tissue by liver biopsy like percutaneous,
transjugular, laparoscopic, and intraoperative, having all of its advantages and disadvantages. The
condition of each patient is studied (indication, risks, and benefits), allowing chosen the appropriate
biopsy technique. The most common approachfor collecting a liver sample is percutaneous LB, either
blinded or under US guidance. Being an invasive technique, the percutaneous biopsy is associated to
complications, occurring in up to 6%, and 0.04% to 0.11% can be life threatening [50].
Liver biopsy ceased to be a ―gold standard‖ when were detected some limitations as the
invasive procedure, risk of complications, patient discomfort, intra and inter-observer variation and
sampling errors. These problems are rare, but they could happen and be very severe, like having
episodes of severe pain, hypotension and intraperitoneal hemorrhage. Furthermore, it is a test which
may have contraindications as uncooperated patients, disorders in coagulation profile, severe ascites,
cystic lesion, vascular tumor, amiloidosis and congestive liver disease [51]. The most common
complication after percutaneous LB is pain. Another disadvantage is the fact that the diagnostic
accuracy of liver biopsy is limited by sampling variability, originating the sampling errors mentioned
above. There is significant variability in the histological assessment of two readings of the same
biopsy by the same pathologist and between two pathologists, even among those who are highly
specialized. This variability is low for the diagnosis of cirrhosis, moderate for earlier fibrosis stages but
high for the activity grades [23].
Concluding, liver biopsy is considered as having the indicated limitations that restrict its role as
a method for screening and longitudinal assessment of liver fibrosis, so it is urgent develop a
reproducible and reliable noninvasive technique to evaluate disease progression in patients with CLD,
and to monitor pharmacological treatment, before it reach a stage too advanced [4].
15
2.2.2 Non-invasive methods
Prognosis of chronic liver disease depends on the amount and progression of liver fibrosis,
because are clinically relevant the detection of significant fibrosis (METAVIR ≥ F2) and detection of
cirrhosis (METAVIR= F4).Non-invasive assessment of liver fibrosis can follow different approaches as
the measurement of liver stiffness by means of transient elastography, the use of serum fibrosis
markers and imaging techniques. The standard expression of the effectiveness of these methods is to
study the area under the receiver operator characteristic curve (AUROC), which plots the sensitivity
over 1 – specificity, taking Fibroscan® as a reference [52, 53].
Ideal non-invasive method should supervise chronic liver disease in a simple way, easily
available, inexpensive, and reproducible. Additionally, it must provide accurate prediction of the full
spectrum of disease severity, sensitivity to treatment effects, and be useful in tracking disease
progression [54].
2.2.2.1 Methods by medical imaging
Imaging techniques are nowadays an attractive way of evaluating fibrosis because they are
noninvasive, having the ability to detect structural changes, which serological-based tests of fibrosis
and inflammation are unable to do. It is possible to diagnose features of advanced chronic liver
disease, using the modalities of ultrasound, computed tomography (CT) or magnetic resonance
imaging (MRI), by recognizing surrogate markers of portal hypertension with a high degree of
sensitivity and specificity [55]. Recent studies have been investigating the US for assessment of the
liver surface, and it was shown to be highly accurate for diagnosing clinically doubtful cirrhosis. This
technique should be investigated for intermediate stages of liver fibrosis. The use of acoustic radiation
force impulses (ARFI) has been suggested to assess liver fibrosis. This imaging technology allows to
evaluate liver stiffness in a region of interest (ROI) involving mechanical excitation of tissue by
acoustic pulses of short-duration while performing a real-time B-mode conventional hepatic US. Other
imaging technique is magnetic resonance (MR) elastography that use a modified phase-contrast
method to assess the propagation of the shear waves within the liver. Some advantages are described
by the potential to analyze the whole parenchyma, as well as the applicability for patients with obesity
or ascites. In the other hand, limitations are represented by the high costs and the fact it is too time
consuming for implementation in clinical practice [49].
In the future, researches should also be made to improve and validate these new promising
imaging technologies (US characteristics, ARFI, MR-elastography), and to define the non-invasive
combination test to optimize accuracy and validation of non-invasive methods for screening fibrosis
and cirrhosis in diabetic patients groups and in the general population [49].
16
For many years, the ultrasound has been used in medicine as a basis for several procedures
in clinical practice. Medical ultrasound imaging relies on the same principles where the field
propagates through the tissue and is partially reflected and scattered due to the inherent
inhomogeneity of most tissues. The backscattered signal is received by the same probe that produces
an acoustic pressure field and converts it into a gray scale image of the organ. As this one imaging
modality is quite sought for diagnostics, the improvement of the quality and diagnostic reliability has
always represented a continuous challenge for many scientists. The hard work is mainly focused in
the acquisition modality itself, by designing new transducers, more sophisticated beam-forming,
compounding or apodization schemes. Another aspect to be considered is the process of post
processing step with suitable signal and image processing tools. In this thesis, only the second
approach will be considered [8].
Today, ultrasound is one of the most used imaging technologies due to the lack of radiation
risk, and it is cheaper when compared with magnetic resonance and computed tomography. In
addition, US images offering a ―cross-sectional‖ view of anatomical structures (tomographic). The
images also can be acquired in real time, providing an instantaneous visual guidance for many
interventional procedures. US mainly using a pulse-echo approach with a brightness-mode (B-mode)
display. As the ultrasound waves penetrate body tissues of different acoustic impedances along the
path of transmission, some are reflected back to the transducer (echo signals) and some continue to
penetrate deeper. The echo signals returned are processed and combined to generate an image.
Thus, an ultrasound transducer generates and receives sound waves [56]. Reflected waves are
partially dispersed and the other part is transformed into heat. The number of returned echo is
influenced by acoustic impedance (density of the medium times the velocity of US wave propagation
in the medium). Air-containing organs have the lowest acoustic impedance, while dense organs such
as bone have very high-acoustic impedance. If two tissues have identical acoustic impedance, no
echo is generated and when interfaces between soft tissue and bone or the lung generate very strong
echoes due to a large acoustic impedance gradient. Interacting waves can suffer reflection (two body
tissues with different acoustic impedances) back to the transducer and refraction refers to a change in
the direction of sound transmission (two tissues with different speeds of sound transmission) [56].
The attenuation is a factor that determines the resolution of ultrasound. This factor is
characterized by stealing of energy from the ultrasound reducing the signal frequency and decreases
of axial resolution with the distance from the emitting aperture. The granular texture patterns in
ultrasound image quality are referred as speckle noise and degrade the image. Collections of sub-
resolvable scatterers are the generators of this noise by the constructive and destructive interference
of waves diffused that refers to scattering centers of size much smaller than the signal wavelength
[49].
17
Table 2 – Comparison of the characteristics image modalities [49].
According to the Table 2 and the contents mentioned above, we conclude that the most
important advantages of ultrasound are portability, cost effectiveness, safety and real-time capability.
Main limitations are low depth-dependent resolution, limited wave penetration, high user dependency
and low image quality due to speckle [8].
Liver fibrosis is the natural response to parenchymal injury in chronic liver diseases and can
result in liver cirrhosis and its various complications. Correct staging of liver fibrosis is essentially in
the decision process for treatment in chronic viral hepatitis, as well as disease prognosis. It is also vital
to monitor disease progression and response to treatment [21].
US can identify the manifestations of CLD such as liver fibrosis and cirrhosis. These changes
are characterized by vascularized fibrotic septa and regenerating nodules. Clinicians should know the
accuracy of ultrasound, especially for patients with high risk of CLD. The more reliable ultrasound
technique appears to be the assessment of liver surface. The studies demonstrated good observer
agreement, high specificity and the usefulness for patients at risk of CLD to assist in determining who
should undergo a liver biopsy (especially focal diseases) [21, 57].
Regarding the appearance of normal liver parenchyma in ultrasound images, this appears
homogeneous interrupted only by normal blood vessels, bile ducts and ligaments. But in the case of
decompensate cirrhosis, the liver presents a heterogeneous parenchyma with increased echogenicity,
irregular contour and high attenuation giving a reduced penetration of the US beam [13].
Characterization of ultrasounds images
Many studies have been performed about quantitative analysis methods in order to improve
the differentiation of tissues. Arise these methods because each individual perceives gray-scale
images in a different way, it leads to the variability in image interpretation whereas. In a quantitative
analysis does not exist subjective interpretation. One quantitative analysis can be based on an
analysis of data from radio-frequency analysis and image texture. However, it is preferable texture
Modality Ultrasound X-ray Computer Tomography MRI
Principle Mechanical
properties
Mean tissue
absorption Tissue absorption Biochemistry
Resolution 0.3 – 3mm 1mm 1mm 1mm
Penetration 3 – 25 cm Excellent Excellent Excellent
Safety Very good Ionizing radiation Ionizing radiation Very good
Speed 100 frames/s Minutes ½ to 1 minute 10 frames/s
Cost 1/8 1/8 ½ 1
Portability Excellent Good Poor Poor
18
analysis due to their easy application, no need for the specialized equipment for data acquisition and
analysis and the lowest storage requirement for data [20].
The texture of an image referred above is a function of the spatial variation in pixel intensities
while the analysis of image texture describes the image properties by textural features. Image texture
can provide information about the structure of physical objects and the texture analysis results in a
numerical value, avoiding discrimination of the lesion. The analysis can be started by image
acquisition through an appropriate scanner and stored in digital format, and continues with the
calculation of a feature descriptor numerical parameters to define a ROI (describe its texture
properties). Is finally determined that the class belongs to the texture of selected ROI [20].
In the case of ultrasound images, if disease processes affect the structure of the tissue, this
should reflect an altered ultrasound signal, which will in turn give in texture features value different to
the normal tissue [20].
2.2.2.2 Other modalities
Methods have been developed to detect and quantify fibrosis. These can be divided in serum
markers and imaging modalities. The transient elastography and serological fibrosis marker (Fibrotest)
– algorithm of five fibrosis markers - have been assessed most frequently. Other algorithm called
enhanced liver fibrosis test (ELF) that is characterized by the use of three fibrosis markers [49]
Serum markers offer an attractive alternative to liver biopsy for patients and physicians. These
markers are classified as direct representing extracellular matrix components and reflecting the
pathophysiology of liver fibrogenesis. Direct serum fibrosis markers are based on combined use
ofvariables representing unique molecular aspects of hepatic fibrogenesis. The indirect use routine
laboratory data and reflect the consequences of the liver damage. As example, is proposed a model
that an aspartate aminotransferase (AST) to alanineaminotransferase (ALT) ratio of >1 is indicative of
cirrhosis. Direct and indirect markers may be used alone or in combination to produce composite
scores [49, 58, 59].
The Fibrotest is the most validated indirect serum marker panel, extensively studied in chronic
hepatitis C but also in chronic hepatitis B, HCV/HIV co-infection and NAFLD. It was studied that this
test is excellent to identify cirrhosis but not so precise in cases of fibrosis. The Fibro index is used for
patients with chronic hepatitis C and uses platelet count. It showed good diagnostic accuracy and high
positive predictive values for significant fibrosis. The Hepascore test, showed good discrimination in
chronically HCV-infected patients. Fibrometers are a family of different blood tests that aims to relate a
morphometric quantitation of the fibrotic area, and the results are validated through an expert system
that can detect erroneous results. Fibrospect is validated in CHC and falls short of sufficient diagnostic
accuracy. Finally in serum markers we have the enhanced liver fibrosis (ELF) is a method that could
be used to screen a range of chronic liver diseases [49].
19
The non-invasive methods are easily to repeat and highly applicable, however serum markers
have limitations. The huge disadvantage of these markers is represented by low accuracy to detect
intermediate stages of fibrosis as compared to cirrhosis. Serum levels of direct markers can be
affected by renal and/or liver failure, extrahepatic sites of fibrogenesis or postprandial state. Apart from
this, simple serum markers are cost-free, easy to calculate and widely available almost everywhere
[49].
Comparing transient elastography, FibroTest and ELF in the same study population,
performed by different approaches and compared the diagnosis of significant fibrosis and cirrhosis
[60]. Cales et al. [61] suggest that the combination of direct and indirect markers might increase
certain advantages and limit other disadvantages. However, in the comparison was not verified a
significant difference [60].
2.2.2.3 Fibroscan®
Over the past decade methods for detecting imaging tissue elasticity were developed,
recognizing the diagnostic value of characterizing mechanical properties when assessing the
presence of disease. Transient elastography (TE) was the first method used in patients with CLD.
Many studies have been evaluating the performance of this method for the diagnosis of fibrosis and
cirrhosis. Fibroscan® is an example and is used by hepatologists all over the world [62].
Liver transient elastography, developed in France by ECHOSENS, is a technique very similar
to ultrasound, designed to measure liver stiffness. The Fibroscan® is composed by an ultrasound
probe, which contains a vibrator in it send. The probe is placed against the skin of the patient through
intercostal spaces (Figure 4) and the vibrator unleashes a shear wave penetrating in the liver [63].
Figure 4 - Liver stiffness measurement using Fibroscan® [62].
20
This method is painless, reproducible, non operator-dependent and without contraindications.
It is liable to repetition, not requiring the intervention of other specialties such as imaging and
pathological anatomy for liver evaluation. The result is instantly displayed in a variable number and the
time required for its execution is about 5 minutes [64].
With the necessity of quantify and monitory fibrosis progression, the Fibroscan® emerged as a
reference tool for liver stiffness measurement, being a relatively new ultrasound technology.
Fibroscan® is based on Transient Elastography that produces a mechanical shear wave with a
consistent frequency and energy. This energy is characterized by low energy ultrasound and
measures the shear wave as it propagates through the skin and liver tissues [65]. Elastography is a
useful adjunct tool for ultrasound diagnosis and its elastograms are images of tissue stiffness with the
possibility to be represented in color, grayscale, or in a combination of this two [66].
The hardness or stiffness observed between pathological and healthy tissue forms the basis
for elasticity imaging. Transient elastography is a combination of B-mode imaging (imaging of acoustic
impedance differences) and Doppler imaging (imaging of flow and movement). The elasticity imaging
from this combination adds a third tissue property defined by stiffness, to the US arsenal for lesion
detection and characterization. The ultrasound allows the determination of the relative stiffness of a
lesion through several different techniques, taking tissue characteristics and the interaction with sound
and extrinsic compression [67].
Elasticity images in Fibroscan®, consists in an estimation of elastic modulus, tracking the
tissue movement during compression to obtain an estimate of strain [68].
Transient elastography is based on the principle of Hooke’s law (referred in Chronic Liver
Disease Chapter), which characterizes a tissue strain response to external stress. The functionality of
this technique is based on a pulse-echo ultrasound acquisition in order to detect the velocity of wave
propagation. The measure of the velocity is proportional to the tissue stiffness, or by a more
descriptive way, a stiffer tissue is identified when is observed a faster wave progression [69].
The Fibroscan® device is the first elastography technique to quantitatively and noninvasively
assess soft biological tissue stiffness in vivo. In this technique, shear waves are generated through the
organ and liver stiffness is computed from their velocity. Vibration-controlled transient elastography
(VCTE) it is based on the controlled generation of a transient shear wave using a servo-controlled
vibration of known frequency and amplitude. Being the velocity computed from mechanical waves, it is
used the following equation that allows deduces the liver stiffness [46].
E = 3ρVs² (2)
In this equation, E represents the Young’s modulus (stiffness), ρ is the density, and Vs the shear
velocity [46].
An ultrasound transducer probe is mounted on the axis of a vibrator with a frequency of 50Hz.
It induces a plastic shear wave that propagates trough the underlying tissues and measures its
velocity which is related to tissue stiffness. The volume measured is at least 100 times bigger than a
biopsy sample, being more representative of the liver parenchyma. The advantages of this technique
21
are the painless, quickness and the ease to perform at the bedside or in the outpatient clinic. Results
are expressed in kilopascals (kPa) and correspond to the median of 10 validated measurements with
a success rate of 60% [27]. The range of liver stiffness values obtained with Fibroscan® starts from
2.5 to 75.0 kPa, with a normal liver stiffness value for healthy individuals of 5.5 kPa. Transient
elastography can measure the liver stiffness in a volume with 1cm wide and 4 cm long, between 2.5
cm and 6.5 cm below the skin surface [4].
Fibroscan® includes a single channel ultrasound analog front end to emit and receive
ultrasound signals and a servo-controlled vibrator (shear wave generation). The ultrasound transducer
is mounted in the probe, which provides data acquired at a very high frame rate during the shear wave
propagation which lasts 80 ms. The elastogram image demonstrates a shear wave, which represents
the strains induced in the liver as a function of time and depth [46].
The reproducibility is an important characteristic of liver stiffness measurement (LSM) with
intra-class correlation coefficients (ICC) of 0.98, which is reduced to lower degrees of liver fibrosis
[27].The interequipment, intraobserver (96–98%) and interobserver agreement (89–98%) of TE has
been showed as an excellent property, however the success rate may change with an observer
expertise, patient BMI and intercostal space [4].
It was concluded that Fibroscan® has good overall accuracy to diagnose advanced fibrosis
and cirrhosis, independently of the underlying etiology. Optimal cutoff values for diagnostic differ
according to some etiologies, which may influence the interpretations of the results of transient
elastography [27]. In other words, the cutoff values may arise from different stages of fibrosis, with
higher cutoff levels corresponding to higher fibrosis stages [4].
The factors such as analine aminotransferase (ALT) may indicate false high values in acute
hepatitis. Extrahepatic cholestasis, hepatic congestion, hepaticamyloidosis and recent food intake
were also found to be associated with a falsely high LSM values. Patients with Non-Alcoholic Fatty
Liver Disease (NAFLD) showed an abnormal BMI and higher LSM values by M probe usually used,
even in the same fibrosis stage. Due to this problem the XL probe was developed, as possible solution
for accuracy in obese patients [70].
The estimation of the degree of hepatic steatosis can be based on the properties of ultrasound
signals acquired by Fibroscan®, for applying this property that affects ultrasound propagation. Known
as controlled attenuation parameter (CAP), intends to perform measures by ultrasound attenuation at
the center frequency of the M probe. In a recent study, CAP was found efficient in detecting low grade
steatosis with a sensitivity of 90% to detect this stage [70].
Transient elastography is a non-invasive, accurate and reproducible test of liver fibrosis and
possibly hepatic steatosis validated in a wide spectrum of liver diseases. Transient elastography is
also useful in predicting patient outcomes [70].
However, TE also has some limitations that are intuitive. Patients with increased tissue
between the skin and liver will variably attenuate the shear wave and the ultrasound waves. Shear
waves do not propagate through liquids because they are elastic waves (only pressure waves can
propagate through liquids). For this reason patients with ascites probably cannot be measurable with
Fibroscan® as far as no physical contact exists between the liver and the intercostal wall. Other
22
examples of restrictions are the high subcutaneous fat or narrow intercostal spaces. This is
extraordinarily accurate, mainly at the limits of liver damage. Despite these limitations, this technique
has great utility in patients with viral hepatitis (majority of the pathologies of the patients studied in this
thesis). [71, 46].
23
3. Ultrasound characterization by image texture analysis
Currently, ultrasound imaging is the most cost-effective and non-invasive among diagnosis
techniques. Liver tissue characterization and classification from ultrasonic scans have been studied
[71, 72, 73] by the availability of the most powerful and cost-effective computing facilities. [74].Due to
characteristics as high applicability in gynecology, cardiology, internal imaging like kidney and
abdominal, portability, easy-to-use and use non-ionizing radiation, ultrasound will continue to play a
prominent role in medical practice worldwide [75]. The variation of acoustic impedance between soft
tissues (e.g. liver) is small, allowing obtain a small amplitude echoes and a good penetration depth
[76].
In the clinical diagnosis of the liver disease, the Ultrasound examination is frequently
performed as a screening step even though a liver biopsy. Due to the aforementioned problems in
relation to the biopsy procedure, the ultrasound scanning is essentially a noninvasive method. Even
so, the US diagnosis is always subjective, because of the clinical observation factor. Some
characteristics of liver sonography are used to evaluate diffuse parenchyma liver diseases that include
changes of ultrasound texture, echogenicity, liver surface, inferior edge and diameter of hepatic and
cystic vein [77, 78, 79].
Texture is a very useful feature for image analysis, due to its visual property being used to
characterize the natural and artificial images. In image analysis process is required the information of
the characteristics of the pixels of an image. This information provides the spatial relationship of a
pixel with other pixels. Texture analysis plays an essential role in classifying objects and outlining the
significant regions of a given gray level image [80].In order to characterize image texture is purposed a
process based on features extracted from ultrasound images.
Statistical methods represent texture indirectly by distribution and relationship between gray
levels of an image. Statistically, the texture is defined by a set of statistics extracted from a large group
of local picture properties. In this work it is applied one of the most used statistical methods called
Gray-Level Co-occurrence Matrix. As an advantage, this method considers second order statistics and
is less susceptible for a change in brightness of the image. The second order statistics takes care of
spatial relationship between groups of two pixels in original image. Generally statistical approaches
are one of the most frequently adopted by various researchers as they perform better in terms of
accuracy and computational complexity when compared with other approaches [81, 82].
Tissue characterization of ultrasound aims studying and developing methods to extract
additional information from the returned echoes, revealing the tissue pathology or abnormality. Is
pretended to identify the assessed pathology and its severity with quantitative criteria (METAVIR
score). Pathologic changes in the biological tissues can be verified in the ultrasound including
parameters like acoustic velocity, impedance, attenuation and scattering, affecting the propagation of
US waves [83].
It is assumed that the changes of the normal liver to cirrhosis can be confirmed to the
echotextural changes of the liver parenchyma, by texture analysis like GLCM and the statistical
24
feature matrix [79].This matrix is represented by features extracted from US images being organized
in statistical and signal processing methods. Statistical features allows the study of spatial distribution
of gray values as previously mentioned and the use of signal processing methods (stepwise
regression analysis and principal components analysis) has a purpose of obtain a reduced set of
features which best represents all data collected from the images [13].
3.1 Texture analysis approach
Texture analysis aims finding a unique way of representing the textures and represents them
in a simpler and unique form, so that they can be used for robust and accurate classification of
objects. Texture performs a significant role in image analysis and pattern recognition [84].
The pattern presented in the texture arises from the repetition, in a deterministically or
randomly way, of local subpatterns. This repetition results in a structure is often important when
discriminating between different textures [85], foreseeing in the same way, a discrimination of levels of
fibrosis, because the texture reveals the pathological changes of the liver. The second-order statistic
in medical field is co-occurrence texture models and demonstrates great classification accuracy. Co-
occurrence matrix texture model have been used in texture analysis for identification of tissue to
detect the abnormality within an organ tissue as well as an identification of different pathological
grades [86].
Texture represents an essential characteristic of images and textural features that have
infinitive applications, such as image processing. A way of extracting these features is by using a gray-
level co-occurrence matrix that contains second order statistical information of neighboring pixels of an
image [87].
This approach of texture analysis is used for methods develop for quantify image texture and
report the image properties by textural features. The reason of performing texture analysis is to define
a set of textural features that will identify the relevant properties of a texture for a defined ROI. Texture
analysis of medical images is an ongoing field of research, intending to obtain the precise way for
detecting lesions by the differentiation between pathological and healthy tissue. There is a variety of
different methods to compute texture features and classify according to the approach used to assess
the relationships of the pixels [20].
The use of statistical features of the texture is one of the early methods proposed and is
considered to be the most widely used method in medical image analysis, more specifically, in the
characterization of ultrasound images [20].
Gray-Level Co-occurrence Matrix is formulated to obtain statistical texture features. A number
of texture features was extracted from the GLCM. Representative classifiers are defined by five
measures called angular second moment, contrast, correlation, inverse difference moment, and
entropy are computed. These measures provide high discrimination accuracy [84].
25
The GLCM matrices extracted from an image database are processed to create the training
data set, useful posteriorly in detection of fibrosis stages [88].
3.2 Description statistics
In statistical texture analysis, texture features are referred like local features computed from
the statistical distribution of observed combinations of intensities at specified positions relative to each
other in the image [84].
3.2.1 US Features
The spatial distribution of gray values is one of the defining qualities of texture. Depending on
the number of pixels that are defined in the local feature, statistical methods can be classified into first
order (one pixel), second-order (two pixels) and higher-order (three or more pixels) statistics. In this
case, the Gray Level Co-occurrence Matrix method is a way of extracting second order statistical
texture features. The difference is that first-order statistics estimate properties of individual pixel
values, so the spatial interaction between image pixels is ignored, while second and higher-order
statistics estimate properties of two or more pixel values in specific locations relative to each other
[89].
When we aim to examine an image using texture analysis, this medical image (2D) can be
treated as a 3D textured surface [12]. In first-order statistical texture analysis, information of texture is
extracted and the frequency of a particular gray-level at a random image position, is measured.
However, the second-order statistical texture analysis offer information of texture based on the
probability of finding a pair of gray-levels at random distances and orientations over an entire image.
The higher-order statistics involves increasing the number of variables studied [20, 12].
Several approaches used in texture analysis have focused on using 2D techniques to compute
features relating to image texture. This type of approaches has been used to describe different image
textures by features and applied to different fields such as discrimination of terrain from aerial
photographs, in vitro classification of tissue from intra vascular ultrasound, identification of protein
distribution in cases of Creutzfeld-Jakob disease (CJD), classification of pulmonary emphysema from
lung on high-resolution CT images, and identification of normal and cancerous [20, 12].
The human eye cannot discriminate between texture pairs with matching second-order
statistics, so it was developed a framework for calculating second-order or pixel co-occurrence texture
information for analyzing images. In this technique, are commonly computed gray-tone spatial
dependence matrices (GTSDM) that represent the probability of finding a pixel with a gray level at
some distance and angle. As previously assumed, second-order description statistics takes into
account both the intensity of the pixels and the interaction with the neighboring pixels. This statistics
26
technique can also get the information on texture is based on the probability of finding a pair of gray-
levels at random distances and orientations over an entire image [12].
Texture features from GLCM are an efficient way to study the texture of an image composed
by different pixels, with different intensities. This method shows how frequently different combinations
of gray levels can occur in an image. A GLCM is a matrix element P (i, j | Δx, Δy) where the number of
rows and columns is equal to the number of gray levels, G, in the image. This matrix is the relative
frequency with which two pixels, separated by a pixel distance (Δx, Δy), one with intensity, i, and the
other with intensity, j. The second order statistical probability values are verified in changes between
gray levels, i, and, j, with a particular displacement distance, d, and a particular angle, ө. When the
intensity levels, G, involves large numbers, the GLCM elements are very sensitive to the size of the
texture samples on which they are estimated and the number of gray levels is often reduced [84].
An input image contains this, G, gray levels from 0 to G – 1, with f(m,n) as the intensity at a
sample, m, and line, n, of the neighborhood (M x N). The matrix GLCM can thus be obtained with the
following expression [90].
𝑃 𝑖, 𝑗 ∆𝑥,∆𝑦 = 𝑊𝑄(𝑖, 𝑗|∆𝑥,∆𝑦),
where, (3)
𝑊 =1
𝑀 − ∆𝑥 (𝑁 − ∆𝑦)
and, (4)
𝑄 𝑖, 𝑗 ∆𝑥,∆𝑦 = . 𝐴
𝑀−∆𝑥
𝑚=1
𝑁−∆𝑦
𝑛=1
(5)
As mentioned previously, the Gray Level Co-occurrence Matrix has proved to be a popular
method and is a way of extracting second order statistical texture features calculated for all pair wise
combinations of gray levels. Haralick [80] proposes fourteen textural features measured from the
matrix to extract the characteristics of texture statistics of images. In this thesis are used five features
described as follows and extracted from ROIs using the ImageJ software [84, 91].
Angular second moment (ASM): evaluates the consistency of textural information and
is known for uniformity or energy. It measures the image homogeneity and is high
when image is excellent or pixels are very similar.
𝐴𝑆𝑀 = .
𝐺−1
𝑖=0
𝑃 𝑖, 𝑗 2
𝐺−1
𝑖=0
(6)
Where, i, j, are the spatial coordinates of the function P (i, j) and, G, is gray tone.
27
Contrast: difference between the values of the adjacent set of pixels considered. This
result shows a low contrast image and high contrast values when the texture is
coarse.
𝐶𝑜𝑛𝑡𝑟𝑎𝑠𝑡 = 𝑛2 . 𝑃 𝑖, 𝑗
𝐺
𝑗=1
𝐺
𝑖=1
, 𝑖 − 𝑗 = 𝑛
𝐺−1
𝑛=0
(7)
Correlation: measures the linear dependency of gray levels of pixels close to each
other. The correlation is an optical method that employs tracking and image
registration techniques for accurate 2D and 3D measurements of changes in images.
In other words, correlation is a value that shows the linear relationship between the
gray levels of pairs.
𝐶𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛 = .
𝐺−1
𝑖=0
𝑖 × 𝑗 .𝑃 𝑖, 𝑗 − 𝜇𝑥 × 𝜇𝑦
𝜎𝑥 × 𝜎𝑦
𝐺−1
𝑗=0
(8)
Inverse Difference Moment (IDM): or Homogeneity assesses image
homogeneousness and the smaller difference between gray values from larger
values. This value is high when local gray level is uniform and inverse GLCM is high.
The contrast weight value is the inverse of the IDM weight value.
𝐼𝐷𝑀 = .
𝐺−1
𝑖=0
1
1 + 𝑖 − 𝑗 2𝑃 𝑖, 𝑗
𝐺−1
𝑗=0
(9)
Entropy: measures the disorderliness of an image. An image with inconsistent texture
have low values for many GLCM elements, however the entropy is very large. In
another words, the entropy measures the loss of information and the image
information.
𝐸𝑛𝑡𝑟𝑜𝑝𝑦 = − .
𝐺−1
𝑖=0
𝑃 𝑖, 𝑗 × 𝑙𝑜𝑔 𝑃 𝑖, 𝑗
𝐺−1
𝑗=0
[84, 91] (10)
Based on the previously information, we can conclude that feature selection refers to
algorithms that select the best subset of the input feature set, whereas methods (feature extraction
algorithms) create new features based on transformations or combinations of the original feature set.
Feature extraction takes in a pattern and produces features values. So, we will perform preprocess
and decompose procedures, as well as the extraction of textural features for tissue characterization
purposes.
28
Set of features
Filtering of non-
informative (descartable)
features
Sequential feature
selection
Subset of features
Test the discriminatory
power of texture features
The following diagram (Figure 5) identifies briefly the steps that this work intends to follow until
get a set of textural features with possible ability to discriminate fibrosis stages.
3.2.2 Influence of Region-Of-Interest depth on image texture
As already mentioned, features can define the texture of the ultrasound image. When these
features are influenced by other factors, the discriminatory power can be affect, emerging the
necessity to study in which way and how they can be influenced. In this work it will be verified in which
way the changes of the ROI position selection can influence the definition of ultrasound texture, and
consequently the possible variation in the staging fibrosis results.
As proved in the work of Ribeiro et al. (2013) [13], a factor that needs to be considered during
feature extraction is the depth of the ROI. This approach limits the flexibility of the technique because
the liver lesions may appear at different depths. Chan and McCarty [93] had also proved that the value
of the extracted features may change drastically under different scanner settings, even if the same
tissue is being imaged [13, 20].
As previously referred, the ultrasound beam causes a depth dependence of the B-mode image
texture. This fact can be explained by the intensity that progressively decreases as the beam
advances through tissues because of scattering, refraction and absorption phenomena. Time gain
compensation (TGC) can be used to amplify the amplitude of echoes in order to compensate for signal
attenuation on the travel path. This attenuation passes through the tissue being an underlying factor
that affects the B-mode image texture. As a strategy to avoid this problem, some studies have
confined the ROI position to a fixed depth to avoid the depth dependency, in case of being a possibility
[20].
With these implications of the ROI coordinates selection in the ultrasound and the features
obtained, were studied with the necessity to evaluate the influence of the ROI depth on the extracted
texture features. In other words, it will be studied the change of features extracted with the depth ROI
Figure 5 - Proposed feature extraction method to textural features study [92].
29
increases. The ROI size selected is similar for all patients. Some examples showed that the
distribution of a texture feature (GLCM) is not significantly correlated with ROI depth and others
showed significant correlation between the ROI depth and the extracted feature, showing a clear
relationship between the extracted features and the ROI depth. The ideal for classifiers stages of the
liver disease would be the little variance of features obtained with increasing depth. So this factor will
be studied in order to understand whether there is influence on the results for images of patients
studied and to conclude how big this variance is [20].
30
4. Procedure and experimental results
The procedure followed in this work will be described in detail along this chapter.
In a particular work [79] is mentioned that the ROI from the liver parenchyma was chosen, and
the hepatorenal textural contrast for each ultrasound image was examined. So, the author proposed a
calculation scheme using the textural parameters being capable to classify the performance of
computer-aided diagnosis of the liver cirrhosis. When applied these parameters, was verified the
following conclusion:
―statistical analysis using the texture properties of the selected ROI from the US image is
widely performed to find out the efficient and accurate way of the computer-aideddiagnosis of the
diffuse liver disease, particularly, liver fibrosis(…) For the quantitative classification of the liver fibrosis,
the further research is needed. (…) The multiple selection of ROI can enhance the statistical stability
of the computer-aided diagnosis of the liver fibrosis, the determination of the fibrosis stages.‖ In other
words, it is apparent that the properties from multiple ROIs may better justify, the fibrosis stages,
because statistically, they represent more adequately the diffuse liver disease. Selecting multiple ROIs
in an ultrasound image aims to register more and different statistical data, covering a larger area of the
liver and likewise study the differences relative to discrimination of fibrosis stages, when selected
different locations.
Other similar work [21] aimed to report the diagnostic performance of ultrasound imaging for
identifying chronic liver disease in patients with high risk. As conclusion was described a diagnostic
accuracy of ultrasound imaging techniques used to identify these patients.
Referring the importance of select multiple ROIs in an image, in this work will be focused the
variation study of ROI selection local, providing information of the texture image and then reveal a
relationship between data collected from ultrasound and the Fibroscan® results. This relation allows
us to compare different discrimination methods of fibrosis staging, based on the ultrasound.
4.1 Methodology
In this chapter will be described some theoretical concepts fundamental for this study. These
concepts will be started with the method of stepwise regression analysis, that was applied with the aim
of first feature selection, checking all candidate variables in the model to achieve a balance between
simplicity and fit.
The used method intends generate smaller set of the variables that can be used to summarize
and maximize the explained variance of the data without losing too much information in the process.
Data fitting aims finding the type of distribution and the value of the parameters that give the
highest probability of producing the features reduced previously.
Prior to obtaining any conclusion it will also be necessary to perform a ROC analysis, creating
curves that assess the quality of diagnostic test.
31
Before the followed methodology is presented, the statistical results will be discussed in this
work.
4.1.1 Stepwise regression analysis
Selection methods, such as stepwise regression methods, have been developed to identify
good subset models, with a reduction of computing (automatic computational procedure). The subset
models are identified sequentially by adding or deleting in the variable that has the greatest impact on
the residual sum of squares. Neither forward selection takes into account the effect that the addition or
deletion of a variable can have on the contributions of others variables to the model [94, 95].
In other words, this analysis could perform description, prediction or revealing of relations
between variables. In this case, it is pretend to perform an isolation of the relevant regressors from a
number of possible ones, choosing only the regressors with impact on the analysis [96].
The candidate variables to analysis give us a general direction and should be included in the
regression model. The actual set of predictor variables used in the final regression model must be
determined by analysis of the data. Finding this subset of regressor variables involves two opposing
objectives. Firstly, we want the regression model to be as complete and realistic as possible and every
regressor that is related to the dependent variable to be included. Secondly, it should be include as
few variables as possible because each irrelevant regressor decreases the precision of the estimated
coefficients and predicted values. The variable selection aims to achieve a balance between simplicity
and fit [97].
Stepwise regression is a modification of the first feature selection so that all candidate
variables in the model are checked to see if their significance has been reduced below the specified
tolerance level. When is found a variable without significance, it is removed from the model. So firstly,
the variables are added and then some are removed. The value of cutoff probability for adding
variables should be less than the cutoff probability for removing variables so that the procedure does
not enter into an infinite loop [97].
The larger the number of input variables, the greater the benefits of the analysis, so the more
important the variable is, the earlier it will be added into the regression model. As the selection is a
focus in this analysis, it is important explain the followed approach, which lead to this process. The
process starts from a model with all predictive input variables and an intercept, considered in this work
the Fibroscan® results. The variables least improving the model are deleted, until no more can
improve the model. The selection criterion is applied to stop the construction of stepwise regression
analysis and determine when a variable was already selected to be deleted from a regression model
[95].
This step of the work was performed in Matlab© using the following formula, which uses fit
regression model by stepwise regression to model the response variableY as a function of the
predictor variables represented by the columns of the matrix X:
32
[b,se,pval,inmodel,stats,nextstep,history] = stepwisefit(X, Y) (11)
Was established a script with the formula presents above and remainder code, in order to obtain the
intended selection of features.
4.1.2 Principal components analysis (PCA)
The PCA is a traditional multivariate statistical method in regression problems, where a first
few of the principal components are used as predictive variables to reduce its number. Principal
component analysis pretends to generate a few linear combinations (smaller set) of the variables that
can be used to summarize and maximize the explained variance of the data without losing too much
information in the process [98].
This method can visualize the complex data by dimension reduction. In addition, more
exploratory data analysis also predicts the models that can be created using the PCA. The first results
obtained with the feature extraction can lead to three problems:
Usually obtains more than three variables that make impossible the graphical representation
of the data.
When we have high correlation between the variables makes it impossible to apply many
statistical methods.
Several variables contain only very few information.
Principal components analysis is capable to avoid all of these problems by transforming the
original variables into a smaller set which are uncorrelated. The features are transformed in a certain
way and then they can be used by other methods. Only the variables with great concentration of
information from lower dimensional data which can be visualized graphically. This method also can
separate the noise from important information [99].
The principal components resulting from the principal component transformation are ordered
of descending order by their level of information but still have the same dimension as the original data.
In order to get to the goal of dimension reduction it will be necessary the use only the first a principal
components, obtaining the results of the matrices aT(n x a) and aP(m x a) respectively, with a model of
the form:
X=aT .aPT + aE (12)
The aE represents the noise which is separated from the relevant information. This model also gives
the percentage of the data total variance that is explained by each model is belonging to a fixed
number of principal components. The fitting as results of the model is evaluated and aims represent
33
the original variables in the new space. Finally, we can obtain potential outliers that are able to
severely distort the results [99].
The moment to apply this analysis, was achieved and a code was created again in Matlab©
that is based on the following formula:
[coeff,score,latent,~,explained]=pca(X) (13)
This script, after performing a reduction of features, returns a vector containing the percentage of the
total variance explained by each principal component and X corresponds to observations and columns
to variables.
4.1.3 Data fitting
In some cases a variable needs a set of observations through of the fitting in a probability
distribution. This necessity arises the need to make forecasts about the future. The method can be
used for fitting a single variable or fitting a multivariate distribution. The principle behind fitting
distributions to data is to find the type of distribution and the value of the parameters that give the
highest probability of producing the observed data [100].
The Curve Fitting Toolbox (Matlab©) allows create a file that can describe objects by using
models. Specifying, this tool creates a surface fitting represented by a mathematical function with
characteristics coefficients, describing it. This fitting assumes the data as a function of three
independent variables, however uses an implicit model [101].
Surface fitting is created by X Data, Y Data and Z Data and the fitting model type (Custom
Equation, Interpolation, Polynomial and Lowess) can be chosen. When choosing the polynomial mode
it is also possible to change the degree of X and Y in order to assess which model best describes the
involved data. For surface data should be selected compatible size data (inputs). Otherwise, if the
sizes are different but the number of elements is the same, then the tool reshapes the inputs to create
a fit and displays a warning in the results pane. In the table data, the X (length n) and Y (length m)
represent the row and column, so the Z correspond to the output (2D matrix) with a size of (m, n) [101,
102].
As a result of this model, we can obtain a model equation, values of the estimated coefficients,
goodness-of-fit statistics and goodness of validation statistics [101]. The statistic can help to determine
how well the model fits the data. The guidelines show the statistics to determine the best fit, by
observation of the resulting numerical values of the following parameters calculated using the
respective functions in an automatic way by the, Toolbox of Matlab©:
34
SSE: sum of squares as a result of error of the fit, where a value closer to zero suggests a fit
that is more useful for pretended prediction, i.e, a value obtained from the following
expression, closer to zero indicates that the model has a smaller error component.
𝑆𝑆𝐸 = 𝑤𝑖 𝑦𝑖 − 𝑦 𝑖 2
𝑛
𝑖=1
(14)
R-square: square of the correlation between the response values and the predicted response
values, measuring how successful the fit is in explaining the variation of the data. A value is
expressed by the following ratio and when it is closer to 1 reveals that a greater proportion of
variance is described by the model, in other words, the resulted value is a percentage
explained of the total variation in the data.
𝑅 − 𝑠𝑞𝑢𝑎𝑟𝑒 =𝑆𝑆𝑅
𝑆𝑆𝑇= 1 −
𝑆𝑆𝐸
𝑆𝑆𝑇
(15)
Where sum of squares of the regression (SSR) is defined as
𝑆𝑆𝐸 = 𝑤𝑖 𝑦 𝑖 − 𝑦 2
𝑛
𝑖=1
(16)
and the total sum of squares (SST) is expressed by
𝑆𝑆𝑇 = 𝑤𝑖 𝑦𝑖 − 𝑦 2
𝑛
𝑖=1
= 𝑆𝑆𝑅 + 𝑆𝑆𝐸
(17)
DFE: degree of freedom in the error. Residuals degrees of freedom are expressed below as
the number of response values n and the number of fitted coefficients m estimated from the
response values.
𝑣 = 𝑛 − 𝑚(18)
Adj R-square: degrees of freedom adjusted to R-square as is defined below. The result can
take on any value less than or equal to one, but when is closer to 1 suggests a better fit.
𝑎𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅 − 𝑠𝑞𝑢𝑎𝑟𝑒 = 1 −𝑆𝑆𝐸 𝑛 − 1
𝑆𝑆𝑇 𝑣
(19)
RMSE: Root Mean Squared Error/standard error that estimates the standard deviation of the
random component in the data. A value closer to 0 indicates a fit more useful for prediction,
obtained from:
𝑅𝑀𝑆𝐸 = 𝑠 = 𝑀𝑆𝐸 (20)
35
Where mean square error (MSE) is defined as:
𝑀𝑆𝐸 =𝑆𝑆𝐸
𝑣
(21)
Coeff: number of coefficients in the model. When we have several similar fits it is possible
look for the smallest number of coefficients to help decide which fit is the best. In other words,
if it has too many numbers, overfitting can occur [101].
In this thesis it was performed the polynomial model of surface fitting, from which it follows the
order, that give us the number of coefficients for fitting, and the degree of polynomial model that
represents the highest power of predictor variables. So this model intends characterize data using
global fit [101].
[]
4.1.4 Receiver Operating Characteristics (ROC)
The ultrasound images collected normally have a number of artefacts that degrade the image
quality and consequently, compromise diagnostic confidence. In the study of medical images study,
quality can be defined in terms of performance in clinically relevant functions, e.g. lesion detection and
classification. When images are studied for medical applications, the correct method of quantifying
visual image quality is through objective evaluation. However, objective evaluation is usually too
inconvenient due to different experience of experts, time consuming and expensive. Hereupon, the
goal of research in image quality is to define and develop quantitative measures [103].
In many cases the ROC analysis is the most used technique for evaluating image quality [104,
105, 106, 107], where a subjective image quality index can be evaluated through the values of area
under the ROC (AUROC). It is required a large number of images to be evaluated that offers a
statistically significant result [103].
In receiver operating characteristic curve, the sensitivity is inversely related with specificity, in
other words, when the sensitivity increases, the specificity decreases across various threshold. It is
observed one plot that displays the picture of trade-off between the sensitivity (true positive rate) and
the 1- specificity (false positive rate) across a series of cutoff points [108].
The shape of the curve between these two fixed points,where sensitivity and 1‐specificity are b
oth zero and both one, depends on the discriminatory ability of the test. AUROC curve is considered
as an effective measure for a valid diagnostic test and it is useful in different aspects [108]:
Discovering optimal cutoff point to obtain minimum incorrect classifying diseased or non-
diseased subjects.
36
Assessment of the discriminatory ability of a test to acceptably pick diseased and non-
diseased subjects.
Comparing two or more tests in usefulness for assessing the same disease;
Two or more observers measuring can be compared in the same test verifying the observer
variability.
The area under the curve is a combination of sensitivity and specificity measurements for
assessing inherent validity of a diagnostic test. When the result of the area equals 1, it means that the
diagnostic test is perfect in differentiating diseased with non-diseased subjects. This implies that both
sensitivity and specificity cannot have errors or false results, so it occurs when the distribution of test
values do not overlap. However, this result is extremely improbable to obtain in practice, so the area
with a value closer to 1 indicates better performance of the test. The next figure shows the different
forms of ROC curve in diagnostic test, where the good diagnostic test represent the major area under
curve, being the curve closer to the value 1 [108].
The probabilities of detecting correct diagnosis are indicated from statistical validity of a
medical test by comparing the true diseased subjects (D+) and true non-diseased subjects (D-). The
response is related in terms of test positive (T+) or test negative (T-). This procedure can be
substituted by a more expensive diagnostic method or a combination of tests. In other cases, may be
also available from the clinical follow-up, surgical verification, biopsy, autopsy, or by panel of experts.
Sensitivity can be called true positive rate (TPR) and represents the conditional probability of correctly
identifying the diseased subjects, while the specificity or true negative rate (TNR) shows the
conditional probability of correctly identifying the non-disease. Other terms that can be mentioned are
Figure 6 – Possible results of diagnostic test in ROC curves (adapted from [109])
37
false positive rate (FPR) and false negative rate (FNR) that perhaps represent the positive test in non-
diseased subjects ROC curve. This plot display the sensitivity (TPR) on y-axis and (1 – specificity)
(FPR) on x-axis for varying cutoff points of test values [108].
Concluding, the ROC curve has following advantages at a particular cutoff analyzed below.
The ROC curve displays all possible cutoff points, being possible read the optimal cutoff for
correctly identifying diseased or non-diseased subjects.
This curve is independent of prevalence of disease since it is based on sensitivity and
specificity.
In the same image, two or more diagnostic tests can be compared.
In some cases sensitivity is more important than specificity or vice versa, and ROC curve can
find a value keeping the other stationary.
Measures can be summarized for determining the validity of diagnostic test such as total and
partial area under the curve.
As already mentioned, if one test has higher sensitivity (higher TPR) it is more accurate for
actually positive patients but it has lower specificity (higher FPR) than the other that is more accurate
for actually negative patients. This problem affects the decision to choose a test to use, because
diagnostic testing would not be needed if the presence or absence of the disease were known.
Therefore, the sensitivity should be used in combination with the specificity, in order to get benefit from
the advantages described above [108].
Overall accuracy is the weighted average of two tests (sensitivity and specificity), where the
first is weighted by prevalence and the second is weighted by the complement of prevalence. There
are many measurements involving sensitivity and specificity to describe the validity of diagnostic tests,
like AUROC, positive likelihood ratio, negative likelihood ratio and overall accuracy. AUROC curve,
can be positive or negative likelihood ratio are based exclusively on sensitivity and specificity, so do
not exhibiting variations with disease prevalence, contrary to overall accuracy (OA) that can vary with
this factor. Concluding, OA as a descriptor of test validity provides a summary estimate to evaluate the
usefulness of a diagnostic test [110].
The Likelihood ratios perform the computation of the ratio between the probability of the
existence of patients with certain disease/fibrosis level and the probability that results of patients
without disease. Providing a summary of how many more times or less likely patients with the disease
can be determined outcome compared to patients without disease. The probability of disease for each
patient hence it is widely used in diagnostic tests and can be calculated. As already mentioned, this
type of ratio can be classified as positive and negative, where positive likelihood ratio gives us the
probability that an individual with the disease has to get a positive test, divided by the probability of an
individual without disease also get a positive test outcome to the stage of disease. It is possible to
classify the resulting value of this ratio, using the following methodology: when numeric result exceeds
1, the test considered positive is more likely to occur in people with disease, while if the result is lower
than 1, it will be less likely to occur in the same people. To be more specific, when the result is greater
38
than 10, increases significantly the probability of the existence of disease in the person and if it is
below 0.1 is considered practically impossible to be classified as having the disease [111].
𝐿𝑅+=𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
1 − 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦=𝑇𝑟𝑢𝑒𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑅𝑎𝑡𝑒
𝐹𝑎𝑙𝑠𝑒𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑅𝑎𝑡𝑒
(22)
Relatively to the negative likelihood ratio, it is considered as the probability of a person with
disease has a negative test divided by the probability of a person without the disease also have a
negative test. In this case, the result is interpreted as negative test, more likely to occur in people with
disease than people without the disease if exceeds 1 and negative test less likely in the same
conditions of disease for less than 1 result [111].
𝐿𝑅−=1 − 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦
(23)
4.2 Procedure according to the methodology
The ultrasound images were obtained from a system US (CX50 Philips, Amsterdam,
Netherlands) with a transducer (C5-1 Philips, Amsterdam, Netherlands) at a frequency of 3.5 MHz,
and with a depth image of 14cm (wave range of the system). A trained operator had stored 62 images
from 13 patients (female and male) with different CLD stages and pathologies, as hepatitis C virus,
hepatitis B virus, cirrhosis, NASH and steatosis. The ultrasound images were collected from patients
aged between 36 and 97 years old like presented in Table 3.
39
Table 3 - Patients information.
All patients had followed hepatology consultations, being well characterized by the specialist
and previously performed a biopsy. On the same day was sequentially performed to the same patients
a test for measurement of fibrosis with Fibroscan® and storage of ultrasound images. These data
were obtained from the same intercostal space and both techniques were based on standard
sonographic parameters of the equipment.
The DICOM images, resulting from ultrasound examinations were stored and its resolution
was characterized by 3 pixels/mm and 8-bit/pixel. From this point, it was possible to initiate the images
analysis in order to get all required data and collect the numerical properties that describe the image
texture. This collecting was performed after opening the ultrasound in ImageJ software, being possible
selecting some regions (ROIs) where informations about features were obtained. All ROIs (set of
pixels in a 2D image selected to be analyzed later) of the analyzed images were selected under
special conditions that could interfere with the final results and therefore on the conclusions. These
conditions imply the selection of different regions on image coordinates, especially at different depths
of ultrasound in order to better study the effect of variation in the desired analysis. So, it was
necessary adopt the following aspects that can also be seen at Figure 7:
Represent the hepatic parenchyma
Select ROIs without image artefacts
Avoid locations of ultrasound with blood vessels
Patient Birthdate Pathology Age
P1 08-12-1968 Cirrhosis 45
P2 16-06-1966 HVC 47
P3 25-11-1915 HVC 98
P4 05-03-1957 HVC 57
P5 18-10-1953 HVC 60
P6 27-06-1975 HVC 38
P7 05-12-1972 HVC 41
P8 17-01-1948 HVC 66
P9 10-01-1976 HVB 38
P10 31-08-1976 HVC 37
P11 24-10-1968 HVB 45
P12 24-03-1965 NASH and steatosis 49
P13 17-07-1970 HVB 43
40
Even though studied few patients, we could collect textural features in a window (ROI) with a
considerable size because a ROI should have 800 pixels2
to provide reliable statistics [112], and from
3 to 5 selections in each image, which allowed us to register a large number of features.
After the selection of a ROI, its features were recorded through the Set Measurements
command allowing us to create a table with these numerical values. For each ROI, its CROP was
stored, surrounding the selected area that was analyzed in order to allow comparing visually the gray
ultrasound through its features.
Texture analysis characterizes the eco-texture of images obtained in B-mode, interpreting the
gray variance of the image, checked when pathological changes occur in the liver tissue. The texture
features are thereby obtained with the help of plugin GLCM [113], from ImageJ software, where the
features are calculated by the intensities of pixels pairs, evaluating their spatial relationship. This
procedure was repeated for each selected ROI and used in opposing directions defined by angles of
0°, 90°, 180° and 270° at a distance between the first pixel and the pixel 6, in which each pixel was
Figure 7 - Example of different ROI selection (52x52 pixels) by ImageJ software. This selection intended
to collect features from different coordinate image of the patient involved, as well as following special
conditions previously mentioned that could interfere with the end results.
41
• US images (DICOM)
• Fibroscan data
Exams and clinical information gathered
from the patients
• ROI selection
• Feature Collection by GLCM (ImgeJ)
Texture analysis • Processing and feature analysis:
• Stepwise Regression Analysis
• PCA
• Fitting
• ROC curves/AUROC
Statistical analysis
analyzed the distance in the four angles mentioned. The distance between the pixels was analyzed to
verify the relationship among the neighbors and their differences.
As a result of this textural analysis, specific values were obtained, referring to the gray scale of
image and creating a values pattern. It was collected a table with the same values from one pixel in
angular directions mentioned above, and so on up to pixel 6.
In order to study the influence of the ROIs position variation, all data was checked and
separated into three different groups. This means that was identified in each image, which would be
the most central ROIs in ultrasound image and ROIs located on the right and left of these (peripheral
ROIs).
After the division of data was performed, we began to examinate the central ROIs, considered
the best for later be used in the classification. During this analysis we intended to firstly select
(Stepwise Regression Analysis) the features that best predicted all the data collected in order to
reduce them (Principal Components Analysis). This data reduction, as previously referred, will provide
a better conclusion and a quicker analysis. The reduction of data was necessary to apply a model
fitting, where was obtained a graphic that describes the variation of the previously reduced data and
that give it the numerical values about the predictive potential of these data entered into the model. In
addition to the numerical values obtained in the fitting process, we could also obtain the polynomial
model that originated the best fitting and compare the results. In other words, the model obtained was
tested and verified if the resulting values were similar to Fibroscan®. On this work section is studied
Figure 8 - Brief layout of the procedure followed in this thesis.
42
the principal focus presented in the literature review, where it intends to verify the deviation of staging
between the results derived from the textural features from ultrasound and Fibroscan® results (our
reference).
Was initiated the analysis of peripherals and central ROIs, after applying reduction methods
and after obtaining new fitting models. Will be verified, the shift of the model test results in relation to
the Fibroscan® results. Will also be studied the ROC analysis for central ROIs, due to its better
results.
The final assessment was performed for central ROIs, due to its better results, by ROC
analysis through a curve and numeric values representing overall accuracy, sensitivity, specificity,
AUROC and others. Obtaining these values and comparing them with reference values from other
studies, will be finished the analysis for these ROIs. All these data processing were performed in
Matlab© and Excel software’s.
4.3 Results
At this point of the work will be presented the values about the selected ROIs (average area,
average coordinates of the selected ROIs in images, and pixels that define the height and width of the
ROIs), and the numerical characteristic values from the texture of these image ROIs which were
analyzed. It will also be described the reduction way of the large group of features collected and
applied the test model from central ROIs in three groups of ROIs (central, right and left). After this, will
be presented the differences in results observed between each of these groups and the Fibroscan®
results, intending to find out whether group values differs too much from the reference values.
4.3.1 Results collection of the features
During the selection process, we tried to select at least 3 ROIs of each image in order to
understand the possible variation verified in relation to axis located in the central zone of the
ultrasound (ideal zone) and the image depth, in other words, the penetration distance of the
ultrasound beam. These selections were characterized by a position with an average in the XY axis of
354.011 and 326.601, respectively. Another important characteristic to mention is the size of the
selected ROI which in this study was characterized by an average width and height of 50.198 pixels
and 50.934 pixels, respectively. Values were design close to these averages to try to obtain sufficient
information through the features of ROIs, in other words, a ROI should not be too small because it
would not provide enough data. On the other hand, a ROI could not be too large because it would be
impossible to select multiple ROIs in some images, respecting the conditions of selection mentioned
above. Thus, the sizes were chosen around these values intending that there is no significant
43
difference in areas between them. Evaluating the values presented above, we can conclude that the
mean area observed in ROIs, is 2556.778 pixels2.
After all images were analyzed, 2670 features were obtained for each patient from 195 ROIs
selected from all patients. After obtaining the intended numerical characteristics, was also stored, part
of the image corresponding to the selected ROI. In other words, a CROP (Figure 9), which allows us
to save only the selected area of the image analyzed, characterized by 34 numerical values described
on the table from Appendix A. 1.
4.3.2 Statistical results
In Appendix A. 2 average features of images obtained from the first patient, are presented, in
order to achieving process the data more briefly.
However, as the difference in position of the ROIs selected is considered a factor to take into
quite consideration in this study, Figure 10 shows how these values were separated in central,
considered the ideal local, and peripheral (right and left). This separation may be important for the
later study of the results, to relate this factor with successful or unsuccessful results.
Figure 9 - CROP saved from an ultrasound image with significant fibrosis.
44
0
100
200
300
400
500
0 100 200 300 400 500 600
Y
X
ROI position of all US (all patients)
Central
Right
Left
Stepwise regression and principal components analysis
Once the differences in selected positions were verified, the process of treatment of features,
was initiated, through the Matlab© software. The stepwisefit model was applied and uses stepwise
regression to model the response variable Fi as a function of the predictor variables represented by
the columns of the matrix F (features). More specifically for the treatment of central features are
inserted in the model mean values of all central features (F) and the values referring to Fibroscan®
(Fi). From all these values, the F values are most significant in the model that is selected.
Then a features reduction was performed using PCA on raw data selected by stepwise
regression, needed for the next steps. The PCA returns a vector representing the percentage of the
total variance explained by each principal component. So, using the same features selected for the
central ROI (XFeatures and YFeatures), we also can reduce the peripheral features (left and right of
the center) and reconstruct the centered features data. This reduction process was evaluated by the
Figure 10 - Representative scheme of the average coordinates in the three groups of selected ROIs. All
the positions (pixels), on axis XY, of the selected ROIs, where blue represents the core ROIs, the red
represents the right features and green represent the left features. Each color also contains a point
called the mean of this color, represented by a greater square and stronger color.
45
values shown in the table presented in Appendix A. 3, as well as the results obtained for the 13
patients.
After the explained numbers were obtained from PCA, we decided to use the first PCA to test
the fitting model (next step), which afford a better explanation of the data.
4.4 Discussion of results
Will be presented in this chapter all the statistical data studied by analysis of surface fitting and
ROC curve.
4.4.1 Surface fitting
In this stage of statistical analysis was necessary to generate surface fittings (polynomial
model) which determined the equation of a polynomial order surface that passes through the
previously reduced points. The surface fitting created a fit object that encapsulated the result of fitting
the polynomial model by the fittype (order polynomial) to the data [101].
We chosen the polynomial fits due the fact that these models include reasonable flexibility for
data that is not too complicated, and they are linear, which reveals a simple process of fitting. These
models can also reveal a disadvantage in high-degree fits becoming unstable, however polynomials of
any degree can provide a good fit within the data range [101]. Therefore, in the data presented in this
study was chosen a polynomial model, because when other models were tested, the results were
considered unusable, being the polynomial model the one which presented the most interesting
values.
The following table (Table 4) presents all polynomial models, possible to obtain in this case, as
well as its values of goodness of fit, so that we can understand what bestFit (best combination of
degrees) should be used in the test model. In this model, the best linear model was put resulting from
this, values can be compared with those recorded by Fibroscan® and checked just how are shifted, if
this is the case.
46
Table 4 - Different combinations of degrees (up to 4) for the polynomial model fitting, the results of
goodness of fit and representative functions of each model.
Fit type SSE R-square DFE Adj R-Square RMSE Linear model
Poly11 73.440 0.253 10 0.103 2.710 f(x,y) = p00 + p10*x + p01*y
Poly12 35.368 0.640 8 0.460 2.103 f(x,y) = p00 + p10*x + p01*y + p11*x*y
+ p02*y2
Poly13 8.457 0.913 6 0.828 1.187 f(x,y) = p00 + p10*x + p01*y + p11*x*y
+ p02*y2 + p12*x*y
2 + p03*y
3
Poly14 5.205 0.947 4 0.841 1.141
f(x,y) = p00 + p10*x + p01*y + p11*x*y
+ p02*y2 + p12*x*y
2 + p03*y
3 +
p13*x*y3 + p04*y
4
Poly21 13.026 0.867 8 0.801 1.276 f(x,y) = p00 + p10*x + p01*y + p20*x
2 +
p11*x*y
Poly22 12.377 0.874 7 0.784 1.330 f(x,y) = p00 + p10*x + p01*y + p20*x
2 +
p11*x*y + p02*y2
Poly23 3.394 0.965 4 0.896 0.921
f(x,y) = p00 + p10*x + p01*y + p20*x2 +
p11*x*y + p02*y2 + p21*x
2*y + p12*x*y
2
+ p03*y3
Poly24 1.983 0.980 1 0.758 1.408
f(x,y) = p00 + p10*x + p01*y + p20*x2 +
p11*x*y + p02*y^2 + p21*x2*y
+
p12*x*y2 + p03*y
3 + p22*x
2*y2 +
p13*x*y3 + p04*y
4
Poly31 3.967 0.960 6 0.919 0.813 f(x,y) = p00 + p10*x + p01*y + p20*x
2 +
p11*x*y + p30*x3 + p21*x
2*y
Poly32 2.467 0.975 4 0.925 0.785
f(x,y) = p00 + p10*x + p01*y + p20*x2 +
p11*x*y + p02*y2 + p30*x
3 + p21*x
2*y +
p12*x*y2
Poly33 2.427 0.975 3 0.901 0.899
f(x,y) = p00 + p10*x + p01*y + p20*x2 +
p11*x*y + p02*y2 + p30*x
3 + p21*x
2*y +
p12*x*y2 + p03*y
3
Poly41 2.096 0.978 4 0.936027 0.724
f(x,y) = p00 + p10*x + p01*y + p20*x2
+ p11*x*y + p30*x3 + p21*x
2*y + p40*x
4
+ p31*x3*y
Poly42 1.365 0.986 1 0.833 1.168
f(x,y) = p00 + p10*x + p01*y + p20*x2 +
p11*x*y + p02*y2 + p30*x
3 + p21*x
2*y +
p12*x*y2 + p40*x
4 + p31*x
3*y +
p22*x2*y^2
After all the models have been registered and presented in the previous table, it was
necessary to understand, by the goodness-of-fit values, which one could justify better the variation of
the features. For this, is shown in the following table (Table 5) with optimal results of fitting, in order to
compare the model results with this table.
47
Table 5 - Ideal results of goodness-of-fit for an excellent data representation by surface fitting [101].
Central ROIs
Statistical methods previously performed allows us to select the model that best fits the data
and our results suggested that selected model (Poly41) was reasonably accurate. We want a model
simple enough with the intention of fit the model to data, but complicated enough to fit our data well.
After observing the values of goodness of fit shown in table 4, through the guideline presented
in the theoretical topic of fitting, we managed to realize what model would best represent the features.
Then these theoretical values and the selected central model were compared, (considered the ideal
due to the proximity of the place to the transducer) so as to evaluate it and justify its choice.
SSE = 2.096
R-Square = 0.979
Adj R-Square = 0.936
RMSE = 0.724
Linear model poly41 (x=4 and y=1) for central ROIs:
f(x,y) = p00 + p10*x + p01*y + p20*x2 + p11*x*y + p30*x
3 + p21*x
2*y + p40*x
4 + p31*x
3*y
With coefficients (with 95% confidence bounds) of:
p00 = 5.943
p10 = 0.227
p01 = -0.089
p20 = -0.088
p11 = -0.009
p30 = -0.006
p21 = -0.003
p40 = 0.000
SSE R-
square
Adj R-
Square RMSE
Ideal
values ≈ 0 ≈ 1 ≈ 1 ≈ 0
48
-10-5
05
1015
-15-10
-50
510
15
4
6
8
10
12
14
XFeaturesROI
CentralYFeatures
ROI
Central
Fib
roscan
RO
I Centr
al
p31 = 0.000
The confidence interval referred above represents the area that has a 95% chance of
containing the true regression (data points) [114].
Evidencing the similarity between the results of the model chosen (poly41) and the ideal
results, the central model (ModelTesting) was applied to test both central and peripheral ROIs. The
Figure 11 shows the best surface fitting for Fibroscan®, XFeatures and YFeatures of central ROIs,
being this, the model that best describes the variance of these features.
The residuals from a fitted model are defined as the differences between the response data
and the fit to the response data at each predictor value. Assuming the model we fitted to the data is
the correct, the residuals approximate the random errors.
A residual is positive when the point is above the surface and is negative when the point is
below the curve. Y values, in the residual plot, represent the distances of each corresponding point
from the curve [114].A plot that contains residuals values and fitted values by the order in which the
data were entered helps to identify abnormal data points [115].
All residual plots provided visual displays for assessing how well the model fits the data, for
evaluating the distribution of the residuals or just for identifying influential observations.
The residuals from a good fit should appear random with no apparent pattern. A pattern can
be a signal that a better model exists, in other words, we should not observe a tendency for
consecutive residuals to have the same sign. When we are dealing with poor quality data, is revealed
in the residuals plot, which has a funnel shape. In this shape, small predictor values yield a bigger
scatter in the response values than large predictor values.
Figure 11–Best surface fitting for central ROIs (model poly41).
49
The residuals values are obtained in the Curve Fitting Toolbox by the following expression
[101]:
𝑟 = 𝑦 − 𝑦
(24)
Or in another word, this value results from:
Residuals= data – fit (25)
These residuals values in Figure 12 did notice a pattern or funnel shape, however exhibited a
random scattered around zero. These residuals, positives and negatives, appeared on the axis with a
range values of [-1; 1] and are larger in the middle of fitted values.
With a confidence interval of 95% from fitting results, it is assumed that does not occurs many
outliers, because was not observed any peak which is too much distanced from the axis in comparison
with the rest of the plot. In other words, the more suitable is the regression analysis, the range of
possible values residuals shall be lowest. The PCA value for central ROIs explains 99.87% of the
variance, so would be expected to the residual values were quite small, and in this case never
reaching the value of 1 on the presented scale.
The values of the previous residual plot, considered minimally more prominent (about 6 values
with a positive or negative deviation of residual axis, greater or equal than 0.2) can posterior be
compared to the values resulting from ModelTesting then applied.
Right ROIs
Continuing to use the polynomial model x = 4 and y = 1, we obtained the surface fitting and its
goodness of fit values presented in Table 6.
Figure 12 -Residuals plot for central ROIs (model poly41).
50
Table 6 - Goodness-of-fit of poly41 in central and right ROIs.
Goodness of fit Central ROIs Right ROIs
SSE 2.096 8.296
R-square 0.979 0.916
Adj R-square 0.936 0.746
RMSE 0.724 1.440
Comparing the data obtained from these two types of ROIs position, we can perceive that the
goodness of fit results from the central ROIs are closest to the ideal values, although the goodness-of-
fit results for the right ROIs also are quite satisfactory.
Linear model poly41 (x=4 and y=1) for right ROIs (equal to central model):
f(x,y) = p00 + p10*x + p01*y + p20*x2 + p11*x*y + p30*x
3 + p21*x
2*y + p40*x
4 + p31*x
3*y
With coefficients of:
p00 = 6.385
p10 = 0.465
p01 = -1.705
p20 = -1.780
p11 = -1.248
p30 = 0.702
p21 = 2.352
p40 = 1.267
p31 = 2.263
The surface fitting for ROIs to the right of central (Fibroscan®, XFeatures and YFeatures for
right ROIs) was based on the same type of model chosen for the central ROIs because these contain
safe features, allowing us to obtain more reliable conclusions.
The following residuals plot (Figure 13) created by right ROIs shows a constant variance, in
other words, values vary in negative and positive way, are not only concentrated on one side of the
axis.
51
For right ROIs, the residuals appear on the axis with range values of [-2; 2].
The increase in the variance of residuals values is noticeable because the regression analysis
result on right ROIs declined a bit, and the PCA model was explained in 94.50% of the variance.
Although it is not a poor outcome, there is a slight difference in these data.
The values of the previous residual plot, considered higher up (about 5 values with a positive
or negative deviation of residual axis, greater than 0.5) can posterior be compared to the resulting
values from ModelTesting applied after.
Left ROIs
Through applying polynomial model x = 4 and y = 1 in the left ROIs, we obtained the surface
fitting and its goodness of fit values presented in Table 7.
Table 7 – All values of goodness of fit for different types of ROI position applied in a poly41 model,
and the ideal values to obtain.
Goodness of fit Central ROIs Right ROIs Left ROIs Ideal fit
SSE 2.096 8.296
9.099 0
R-Square 0.979 0.916
0.907 1
Adj R-Square 0.936 0.747
0.722 1
RMSE 0.724 1.440
1.508 0
Figure 13 - Residuals plot for right ROIs (model poly41).
52
The table presented above (Table 7), allow us the observation of the type of ROIs which most
closely approximates of the results, defended as the ideals is the center, wherein the results can be
rather close to the desired.
In respect of ROIs peripheral, it was observed that the ROIs to the left of the central, in spite of
presenting quite satisfactory values, are from all that most diverges from the ideal results.
Then, it was presented again the model used as well as the numerical values for each
combination of degree.
Linear model poly41 (x=4 and y=1) for left ROIs (equal to central model):
f(x,y) = p00 + p10*x + p01*y + p20*x2 + p11*x*y + p30*x
3 + p21*x
2*y + p40*x
4 + p31*x
3*y
With coefficients of:
p00 = 5.495
p10 = -1.314
p01 = -0.819
p20 = 1.241
p11 = -3.815
p30 = -1.564
p21 = 9.187
p40 = -1.053
p31 = 5.107
The following residuals plot (Figure 16), created by left ROIs, also shows a constant variance
where the values vary in negative and positive way, are not only concentrated on one side of the axis.
Figure 14 - Residuals plot for left ROIs (model poly41).
53
For left ROIs the residuals appear on the axis with a range value of [-1; 2.5].
The increase in the residuals values variance is noticeable because the result of the
regression analysis on ROIs to the left of the central declined a little, so that the PCA model was
explained in 95.12% of the variance. This value is similar to result PCA of right ROIs, so although it
can be a good result, there is a slight difference in these data.
However, there is a residual value that causes some doubt about the existence of outliers,
with a residual value greater than 2, because in this residual plot distances itself slightly from the other
points.
The values of the previous residual plot, considered higher up than central data of images
(about 4 values with a positive or negative deviation of residual axis, greater than 0.5) can posterior be
compared to the resulting values from ModelTesting applied after.
4.4.1.1 Comparison of Fibroscan® results with textural features
results
Moving forward to the next step of data processing, the polynomial models were presented,
through the resulting model of the central ROIs. This ModelTesting makes possible to get the table
below (Table 8), in order to realize how effective would be to perform a classification of relatively low
fibrosis stages. The assessment of this classification was then performed through the comparison of
features originated from different positions with the results considered the reference (Fibroscan®) for
the patients in the study.
Table 8 - Fibroscan® results vs. right features, left features and central features (shifts from
Fibroscan® results).
Patients Fibroscan® Features
Central Shift Right Shift Left Shift
P1 4.9 4.90 0.00 -23.08 18.18 90.81 85.91
P2 5.4 5.16 0.24 6.61 1.21 -2.21 3.19
P3 3.6 3.60 0.00 -14.37 10.77 4.14 0.54
P4 6.9 6.11 0.79 5.57 1.33 4.83 2.07
P5 14.8 14.80 0.00 -1.13 13.67 0.01 14.79
P6 5.4 5.51 0.11 6.35 0.95 5.37 0.03
P7 5.2 5.94 0.74 4.26 0.94 6.16 0.96
P8 6.1 5.62 0.48 44.93 38.83 4.66 1.44
P9 5.3 6.05 0.75 3.56 1.74 8.65 3.35
P10 7.4 7.48 0.08 7.08 0.32 5.97 1.43
P11 7.8 7.60 0.20 5.36 2.44 5.12 2.68
P12 4.6 4.62 0.02 4.10 0.50 6.07 1.47
P13 3.8 3.81 0.01 -0.64 3.16 3.80 0.00
54
After observing the Table 8, we can verify that the staging results for central ROIs through the
textural features are closest to the Fibroscan® values, because it never showed a shift value of 1, so
the largest deviation is quantified to 0.79. There is a small exception for patients 6 and 13, which have
a smaller shift in the left ROIs results (better than central ROIs), however, is not considered a
difference of great significance.
Both right and left ROIs showed a peak of large deviation (highlighted in gray). These peaks
matches with the fact that the residuals plot of each these positions have also a higher peak, as
showed in Figure 15. As the residuals values treat the estimation error of the model between data and
fitting, can be related with the results from right and left ROIs. In the same way, the data positioned on
the right of the central also have a highlighted peak that is probably the greatest deviation of these
ROIs with a value of 38.83kPa. This deviation is less than the largest deviation in the left ROIs
(85.91kPa), and the peak is also less intense. In the case of central ROIs, is not observed any peak
too much detached by the apparent reason that there are no great shifts from the target value.
In the case of negative values (highlighted by red), mainly observed in the right results, is
immediately apparent that these classifications are incorrect.
The patient who registered, by Fibroscan®, the highest fibrosis value (14.8 kPa), may have
influenced the results because it is too different from the other values. However, although we wanted
focus on the stage F2 or less, we also intended to include some different stage to try perceive
discrimination differences.
55
With the intention of study all 13 patients with different fibrosis levels, we realized that
comparatively to the reference values (Fibroscan®), the records of the central ROIs, do not show in
any patient a shift of more than 1 value. However, the same cannot verify in the peripherals ROIs,
because each group presents only four patients with a shift below 1. In other words, only 30.77% had
a diagnosis with an associated error of less than 1. By this result it is explicit the difference in results
between the features obtained from the central and peripherals ROIs, so there is not any doubt that
central features are significantly more useful for diagnosis.
As we can observe in Figure 8 and Table 9, is revealed the difference of positions between
groups of ROIs created. These positions means that the central ROIs is quite central to the place
considered ideal, due to the proximity that it has with the beginning of incidence of the ultrasound
beam. So, it is logical that these ROIs have revealed a discriminatory power similar to that intended.
However, the right ROIs are quite distant from the desired position, both in the X-axis and the Y-axis,
Figure 15 - Residual Plots (residual values vs. fitted values) for central, right and left ROIs groups.
56
taking a deviation of 62.485 and 44.517, respectively, from central ROIs. That fact may be an indicator
that the position of the selected ROIs can influence the usefulness of features. The majority of the
right ROIs are located at a greater depth (penetration of the ultrasound beam).The left ROIs are also
quite diverted from central in the X-axis (66.490), however, in the Y-axis are significantly less diverted
(19.696). Concluding, the depth factor is an indicative of lower efficiency, such that the ROIs right,
reveal 4 negative staging (impossible) and one too much diverted from ideal. While the left have only
one negative and one stage too high.
Table 9 - Mean values for the positions of divided ROIs and all ROIs on the XY axis.
ROIs type
Position (Mean) Total Central Right Left
X 354.011 342.819 280.333 409.309
Y 326.601 356.619 312.101 336.923
57
4.4.2 ROC analysis
This analysis was performed to compare the Fibroscan® and the texture analysis results,
simultaneously. A ROC curve must be present above the positive diagonal of the unitary plan, so that
it can be considered as a valid result.
To analyze the results obtained by this method was essential to select a suitable cutoff to our
study. A cutoff with a specific value of 6 was established because, according to METAVIR score, this
value initiates the range of considerable levels of fibrosis for performing tests or monitoring. The cutoff
was established in order to optimize the relationship between sensitivity and specificity.
Values obtained from perfect tests which are above the cutoff are always indicating the
disease. On other hand, the values below the cutoff are always excluding the disease. However, this
perfection does not exist in real life and therefore diagnostic procedures can make only partial
distinction between patients with and without disease or above and below the cutoff. Efficacy is based
on the results of a diagnostic test agree, in some statistical sense, with patients actual state of health
or disease [116].
As evidenced in the previous analysis, the central model is the best model and especially,
when applied the central ROIs. Due to this fact, it was decided to use only the central ROIs in this
analysis of ROC curves, because they reveal without any doubt, the best results.
According to a previous study [117], one of the most used measures is the AUROC curve that
combined measurement of sensitivity and specificity. In other words, it can measure the performance
of a diagnostic test and is interpreted as the average value of sensitivity for all possible values of
specificity. Area under curve can take a value between 0 and 1, however the practical lower limit for
the AUC of a diagnostic test is 0.5 showing some ability to discriminate between subjects with and
without a particular disease. Because sensitivity and specificity are independent of disease
prevalence, AUC is also independent of disease prevalence. This area is frequently presented along
with its 95% confidence interval (CI), so an AUC value obtained from different patients is not a true
value, but a value from a sample that is subject to statistical error.
Table 10 -Assessment to ROC curve performance [51].
AUROC value Performance
0.9 - 1 Excellent
0.8 – 0.9 Good
0.7 – 0.8 Reasonable
0.6 – 0.7 Low
0.5 – 0.6 Bad
58
As the AUROC value is considered one of the most important parameters to reveal the
performance of a test, we can conclude that the ROC curve for the central ROIs has a fairly interesting
result. According to Table 10, the Matlab© script found a value of 0.914 with SE=0.095 (Standard
Error) and 95% CI from 0.727 to 1. Once again through these values range can be mentioned that the
AUROC value is very interesting.
In order to achieve a better interpretation, the data were calculated for the distances between
the ROC curve and the point (0, 1) in the plot or where the sensitivity is maximal. These distances
allowed to verify the existence of a point (threshold) that represents in the best way the test. An
optimal threshold is the point that gives maximum correct classification. In the following table (Table
11) is present the consequences of a thresholds which are not ideals [108].
Table 11 - Expected characteristics for different threshold types, adapted from [118].
Low threshold High threshold
False positive rate High Low
False negative rate Low High
Specificity Poor Good
Sensitivity Good Poor
This method, to obtain an optimal threshold, gives equal weight to sensitivity and specificity
and no prevalence constraints. So any distance can be calculated by the definition [108]:
𝑑 = 1 − 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 2 + 1 − 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 2
(26)
The expression will obtain the optimal cutoff point capable to discriminate between patients
with disease and non-disease, calculating the distance for each observed cutoff point, and locate the
point where the distance is minimum [108].
59
Table 12 - Minimum distance (maximum correct classification) between ROC curve and the point (0, 1).
Sensitivity 1-specitivity Minimum distance
0 0 1
0.2 0 0.8
0.4 0 0.6
0.6 0 0.4
0.8 0 0.2
0.8 0.125 0.236
0.8 0.25 0.320
1 0.25 0.25
1 0.375 0.375
1 0.5 0.5
1 0.625 0.625
1 0.75 0.75
1 0.875 0.875
The sensitivity and 1-specificity values were collected from the ROC curve in the Figure 16.
The results (Table 12) from the formula reveal three interesting numbers lower than 0.3 (1, 2 and 3 in
Figure 16) that may be related to the three values resulting from central ROIs of Table 7which do not
show any deviation of the Fibroscan® values. However, it is possible to observe a distance that stands
out with the lowest value (0.2) represented in the Figure 16 by the number 1.
60
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
False positive rate (1-Specificity)
Tru
e p
ositiv
e r
ate
(S
ensitiv
ity)
ROC curve (AUC=0.9144)
1
As can be seen in Figure 16, all points are represented by the ROC curve, defining the
performance of the data in the test of general way. However, is also possible to compare the accuracy
of some points [119], previously defined as the closest to optimal thresholds. The three points
highlighted in the Figure 16 and numbered from 1 to 3, for example, can be compared by the TPR and
FPR values. Starting with points 1 and 2, we can see that point 1 is represented by a FPR value of
zero at the same level of TPR values, thus indicating that the point 1 is more precise than point 2.The
point 3 even though already be represented by some FPR percentage, is the first of all the points that
has a maximum value of TPR. Therefore it is noticeable that comparing points, are at the same level
in one of the axis (sensitivity), the other axis can be sufficient to identify the best point.
In Table 8, the Fibroscan® information shows that 8 in 13 patients are below the cutoff chosen
and 1 patient not being below 6kPa, is fairly close (steatosis = 6.1kPa). As the Fibroscan® values, the
central ROIs indicate the same number of people with steatosis below 6kPa (8 patients) and also a
person with a value quite close to 6 (steatosis = 6.05kPa).
Figure 16 - ROC curve for central ROIs.
2
3
61
The following figure can also be considered an interpretation of the ROC curve (Figure 17).
This figure shows that the sensitivity (probability to detect disease when the patient has the disease),
is nonexistent initially and became maximum, with the increase of fibrosis value. That fact may
indicate that the test is characterized by a greater efficacy from significant fibrosis values, because the
sensitivity is represented by a maximum value after a cutoff of 7.5 kPa.
As expected, the parameter specificity (probability to detect insignificant fibrosis stage when it
is significant) behaves contrary to sensitivity. Presenting a maximum for low fibrosis values (up to 3.6
kPa), which are not considered alarming according to the METAVIR score. After this value, with the
increase of fibrosis, the specificity goes to a lower significant level.
The efficiency observed in the graphic reveals an efficiency peak in fibrosis of 5.51 kPa, which
is understandable, because most of the patients show stages very close to this value in Fibroscan®.
This maximum efficiency is very close from the cutoff applied (considered significant) which may be an
indicator of good performance.
The following table allows us to conclude which factors can influence the sensitivity/specificity
and positive/negative predictive values. These factors might explain some results presented below.
Interpreting Table 13, we can conclude that optimal results would be obtain positive tests in patients
with the disease and negative tests in people who have developed fibrosis in a level below the cutoff
applied (6kPa).
Figure 17 - Characteristics resulting from ROC analysis and the cutoffs created.
62
Table 13 - Possible results for diagnostic test [110].
Relatively to the central model (best model) was obtained the performance parameters from
the diagnostic test (Table 14). The great majority of these parameters, reveals good results in
comparison with desired values.
Table 14 – Results obtained from ROC analysis (central model).
Diagnostic test performance
parameters
Obtained
results
Accurate results (CI)
Prevalence 38.5% (15.1% - 67.1%)
Sensitivity 80.0% (49.0% - 97.3%)
False Negative Rate 20.0% (4.7% - 49.0%)
Specificity 100.0% (71.7% - 100.0%)
False Positive Rate 0.0% (0.7% - 22.1%)
Youden’s Index 0.800 1
Positive Likelihood Ratio Inf (Inf - Inf) - Large (often conclusive) increase in
possibility of disease presence
Negative Likelihood Ratio 0.200 (0.158 – 0.252) - Moderate increase in possibility
of disease absence
Predictivity of Positive Test
(Precision) 100.0% (71.7% - 100.0%)
Predictivity of Negative Test 88.9% (58.2% - 100.0%)
Accuracy 92.3% (62.1% - 100.0%)
Starting with the total corresponding to prevalence, it is perceptible that the percentage
reveals a low group of people with a value above fibrosis of 6kPa. Such as already was observed
previously there are 8 in 13 patients below this level.
The False Positive Rate resulted in a percentage of zero, meaning that there are no cases in
which a healthy person displays tests positive for the significant fibrosis. On the other hand, the False
Affected (D+) Healthy (D-)
Positive Test (T+) True positive False positives Positive predictive value
Negative Test (T-) False negative True negatives Negative predictive value
Sensitivity
Specificity
63
Negative Rate exhibited some percentage (20%), although was not very high revealed the possible
existence of cases where sick people (above the level of fibrosis 6) show negative tests for the
disease. This case should be completely avoided since it represents some danger to the patient. This
means that this diagnostic test would never go introduce false patients, but would go possibly
introduce some cases of healthy people, when this would not be the case.
As a consequence of the FNR percentage, the sensitivity is represented by a percentage of
80% because this parameter reveals the conditional probability of detecting the disease while there is
in fact the liver disease. As expected the specificity is defined at 100% due to nonexistence of FPR
percentage, because it represents the conditional probability of detecting the normal liver while the
liver is indeed normal. Both sensitivity and specificity do not depend on the prevalence, concerning
only with the condition on the patient either having or not having the disease [120].
The Youden’s Index is the point on the ROC curve which is farthest from diagonal line and
aims maximize the difference between TPR and FPR [108]. Our Youden's Index indicates good
performance of the diagnostic test, being very close to the ideal maximum.
Likelihood ratio for a positive test (LR+) is defined by the expression presented in the
respective chapter. That is, the result from this formula is inconclusive because it is infinite
(denominator zero), however, is more positive test likely to occur in people with the disease than in
people without the disease. For likelihood ratio for a negative test (LR-) was observed a value of 0.200
with a CI (0.158 – 0.252). This means that the probability of having a negative test for individuals with
significant fibrosis is 0.2 times or one-fifth, which enhances the result verified in FNR. In another
words, individuals without the disease are about five times more likely to have a negative test than
individuals with the disease [111].
Accuracy or overall accuracy is weighted by the prevalence of the outcome in the study
population and represents the probability that a patient will be correctly classified by a test.
As a descriptor of test validity, the overall accuracy provides a summary estimate to assess the
usefulness of a diagnostic test. However, the prevalence-dependent of overall accuracy obviates its
value as a descriptor of test validity. In this study, although the accuracy reveals an interesting value,
maybe it was influenced by the fact that the prevalence percentage not be too high.
Through the observation of table 13, we can interpret the meaning of Predictivity of Positive
Test or PPV and Predictivity of Negative Test or NPV values. In case of PPV value (100%), we can
say that the study has full effectiveness by revealing the probability of a patient actually has the
disease, given a positive test result. On the other hand, the NPV shows the probability that the patient
does not actually have the disease, given the negative test result. This means that 88.9% of people in
the study who did not have a level of fibrosis greater than 6 kPa were reported as negative test and
1,443 from 13 individuals were reported as positive test when they were not above 6 kPa. These
values cannot be compared with the sensitivity and specificity results, because they are influenced by
the prevalence of significant fibrosis in patients under study [121].
Other important value is the accuracy being that these two measures exhibit a value quite
close to the maximum ideal.
64
To understanding the viability of the numbers obtained in the ROC analysis (Table 14), these
were compared to the performance tests Fibroscan® (our reference point) in patients with HCV and
HBV, because it is the condition that most patients present in this study. Some values are given in the
Table 15, showing several studies performed with varying cutoffs relatively close to the applied and by
different authors.
Table 15 - Diagnostic performance of transient elastography for significant fibrosis in chronic hepatitis
B and C.
Authors
Degos
et al.
2010
[122]
Friedrich-
Rust et al.
2010 [60]
Lesmana
et al. 2011
[123]
Myers
et al.
2010
[124]
Sporea
et al.
2008
[125]
Transient
elastography for
significant fibrosis
(≥F2) in chronic
hepatitis B or C
Etiologies HBV HCV HBV HCV HCV
Patients(n) 284 36 117 85 161
Cutoffs
(kPa) 5.2 7.10 5.85 7.7 6.8
AUROC 0.78 0.80 0.61 0.73 0.773
Sensitivity
(%) 89 79 60.3 65 59.6
Specificity
(%) 38 50 63.6 67 93.3
PPV (%) 50.5 79 73.3 77 98
NPV (%) 82.9 50 49.1 52 30.1
Likewise, the specificity value recorded in this study is superior to all other recorded in the
table and the sensitivity value, although it is not greater than all of the examples, is superior to most of
them.
In respect of PPV and NPV values, can be compared and verified that both values of the
study, 100% and 88.9%, respectively, are for the great majority substantially higher, which indicates
us more effectiveness.
The performance of a noninvasive diagnostic method is evaluated by calculation of the area
under the receiver operator characteristic curve (AUROC), taking Fibroscan® as the reference
standard. However, as we can observe, despite the studies presented were all performed at a higher
number of patients, the AUROC values are all lower than the value obtained in this study (0.914),
perhaps because the Fibroscan® reveals less effectiveness for low levels of fibrosis in patients with
HCV and HBV as was proven in the following references. Fibroscan® analysis is an imperfect
reference standard. According to previous studies [126, 127], the ability of TE to quantify liver fibrosis
65
in patients with chronic hepatitis C have been confirmed [122, 128] and also confirmed in patients with
hepatitis B [129, 130], in other words, TE detects more accurately cirrhosis than significant fibrosis.
With this, is necessary to take into account some margin for doubt in relation to the values resulting
from the Fibroscan®, because the most of the studied individuals were characterized by low fibrosis
levels. This fact may account for some of the FNR from texture features reports in the assessment of
steatosis in our study.
66
5. Conclusions
Once presented the high incidence of health problems associated with the increased of
fibrosis levels that happen nowadays, and the advantages of the techniques that were discussed in
this study (US images and Fibroscan®), we proposed an analysis that attempted to use a technique,
with the possibility of applying on a classification method. After obtained, the results from this study,
we compare them to the reference results (Fibroscan®), and some differences were observed. This
relation allowed us to compare different discrimination methods of fibrosis staging that are based on
the ultrasound.
Comparing the Fibroscan® results and the results from the ModelTesting with central ROIs
was verified that the staging results from central ROIs through the textural features are closest to the
values of Fibroscan®. The same cannot be verified in the peripherals ROIs. In other words, the co-
occurrence matrix revealed discriminatory power differences in the three groups of ROIs, where
peripherals ROIs showed some significant shifts from reference values by presenting a lower
discriminatory power compared to the central ROIs. In conclusion, there is not any doubt that central
features are significantly more useful for diagnosis, proving the influence of selection position.
In ROC analysis, the central ROIs presented an interesting AUROC value. The maximum
efficiency was very close from the cutoff applied (considered significant) which may be an indicator of
good performance. The great majority of performed parameters in ROC analysis, revealed good
results, in respect of desired values. In this study, overall accuracy revealed an interesting value that
perhaps had been influenced by the fact that the percentage of prevalence was not too high. Positive
Predictive and Negative Predictive Values (influenced by the prevalence), revealed that the study had
full effectiveness.
Future Work
Here, a small number of images were taken and maybe were not sufficient to statistically
prove this thesis work results. In order to achieve definite conclusions, it will be necessary to study a
larger number of images. Images of other body parts can be taken to see if the same results also
apply to them or not. Further work in this field should be done to focus on reducing or eliminating the
role of human expert, due to his subjectivity that is always present, and not on finding new textural
features. In an examination of Fibroscan®, the region of interest cannot be chosen, therefore, in the
association of these two techniques based on ultrasonography would be possible to obtain one more
advantage for different diagnoses.
67
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74
Appendix
A. 1
Table 16 - Description of features extracted from US images.
Feature name Definition
Area Area of ROI selection in square pixels.
Mean Average gray value within the ROI (sum of the gray values of all the pixels divided
by the number of pixels).
StdDev Standard deviation of the gray values used to generate the mean gray value.
Mode Most often occurrent gray value.
Min and Max Minimum and maximum gray values.
X and Y Center point of the ROI (centroid)
XM and YM Brightness-weighted average of the x and y coordinates all pixels in the ROI.
Perim. Length of the outside boundary of the selection.
BX and BY Coordinates of the upper left corner of the rectangle.
WidthandHeight Width and height of the ROI selected.
Major and minor Primary and secondary axis of the best fitting ellipse.
Angle Angle between the primary axis and a line parallel to the X-axis of the image.
Circ. Describes the shape of the selection, in which value 1 reveals a perfect circle and
0 indicates a more elongated shape.
Feret The longest distance between any two points along the selection boundary.
IntDen Sum of the values of the pixels in the ROI or the product of Area and Mean.
Median Median value of the pixels in the ROI.
Skew Third order moment about the mean.
Kurt Fourth order moment about the mean.
%Area Percentage of pixels in the ROI that have been highlighted in red.
RawIntDen Sum ofpixelvalues.
Slice Position in the stack or hyperstack of the selection.
FeretX and
FeretY Starting coordinates of the Feret diameter.
FeretAngle Angle (0°-180°) of the Feret’s diameter.
MinFeret Minimum caliper diameter of the Feret’s diameter.
AR Aspect ratio of the particle’s fitted ellipse.
Round Inverseof Aspect Ratio.
Solidity [𝐴𝑟𝑒𝑎]
[𝐶𝑜𝑛𝑣𝑒𝑥𝑎𝑟𝑒𝑎]
A. 2
75
Table 17–Average of extracted features from the selected ROIs in different images and texture of this
area of ultrasound,only for the first patient,as well as the spatial relationship of the first pixel.
Patients P1
ROI selectioncharacteristi
cs
Area 2459.333
Mean 40.19
StdDev 10.595
Mode 40
Min 12
Max 74.667
X 349.5
Y 354.917
XM 349.905
YM 354.698
Perim, 198.333
BX 324.5
BY 379.5
Width 50
Height 49.167
Major 56.607
Minor 55.29
Angle 15
Circ, 0.788
Feret 70.13
IntDen 99075.333
Median 39.667
Skew 0.255
Kurt -0.307
%Area 100
RawIntDev 99075.333
Slice 1
FeretX 324.5
FeretY 183.5
FeretAngle 135.48
MinFeret 49
AR 1.023
Round 0.977
Solidity 1
Texture features from GLCM plugin
(averages of some images for the first
patient)
0°
Angular SecondMoment 0
Contrast 24.878
Correlation 0.01
InverseDifferenceMoment 0.232
Entropy 6.377
90°
Continuation of the table
76
Angular SecondMoment 0
Contrast 86.997
Correlation 0.008
InverseDifferenceMoment 0.135
Entropy 6.838
180°
Angular SecondMoment 0
Contrast 24.762
Correlation 0.01
InverseDifferenceMoment 0.232
Entropy 6.373
270°
Angular SecondMoment 0
Contrast 74.728
Correlation 0.008
InverseDifferenceMoment 0.158
Entropy 6.73
77
A. 3
Table 18–The model assessment and results obtained with PCA for X and Y features, wherein the P1,
P2, P3… P13 are the patients studied.
ROI position
Central ROI Right ROI Left ROI
Explained
results
the first PCA explain
99.87% of the variance
the first and second PCA
explain 94.50% of the
variance
the first and second PCA
explain 95.12% of the
variance
Features
reduced
X
P1=-13,4579670329670
P2=3,91703296703297
P3=7,39917582417587
P4=0,342032967032971
P5=16,6848901098901
P6=-0,0293956043956232
P7=-6,45796703296703
P8=-6,74368131868133
P9=1,82774725274727
P10=0,342032967032967
P11=-6,45796703296703
P12=0,342032967032974
P13=2,29203296703298
P1=-14,9295954084616
P2=0,490859141538461
P3=13,3301448515385
P4=-2,13414085846155
P5=11,7825258115385
P6=3,11585914153846
P7=0,0603035815384591
P8=-9,38414085846157
P9=2,81585914153847
P10=6,11585914153846
P11=-0,495251968461547
P12=-7,38414085846154
P13=-3,38414085846153
P1=-15,4104506584615
P2=11,9531857015385
P3=-0,337723388461537
P4=-1,13772338846154
P5=13,7511655015385
P6=-0,871056718461538
P7=-1,63772338846154
P8=-6,13772338846154
P9=3,23727661153846
P10=1,61227661153846
P11=-8,13772338846154
P12=-0,0127233884615382
P13=3,12894328153846
Y
P1=8,45631868131863
P2=0,456318681318644
P3=-14,6151098901104
P4=-1,04368131868137
P5=10,5277472527476
P6=4,81346153846163
P7=-7,24368131868135
P8=-5,61510989011038
P9=-0,186538461538366
P10=-15,8436813186814
P11=-12,2436813186814
P12=14,9563186813186
P13=17,5813186813186
P1=21,4685953076923
P2=-6,08822289230773
P3=-20,5703657923077
P4=-4,71322289230773
P5=7,95344380769229
P6=-5,21322289230772
P7=18,8423327076923
P8=-49,9132228923077
P9=17,0867771076923
P10=-25,7132228923077
P11=5,28677710769227
P12=-0,713222892307732
P13=42,2867771076922
P1=-18,5472611076923
P2=3,72546619230769
P3=19,8163752923077
P4=8,81637529230768
P5=6,37193089230766
P6=3,68304199230766
P7=-9,18362470769234
P8=-3,73918030769232
P9=-22,5586247076923
P10=0,816375292307668
P11=-1,51695800769232
P12=-1,43362470769233
P13=13,7497085923077
78