978-1-4244-9306-7/11/$26.00 ©2011 IEEE 1801

2011 4th International Congress on Image and Signal Processing

A novel quantitative measurement for thyroid cancer detection based on elastography

Jianrui Ding, H.D.Cheng, Jianhua Huang, Yingtao Zhang

School of Computer Science and Technology Harbin Institute of Technology

Harbin, China

H.D.Cheng

Department of Computer Science Utah State University

Logan, USA

Chunping Ning Department of Ultrasound

Second Affiliated Hospital of Harbin Medical University Harbin, China

Abstract— At present, the widely methods used to evaluate elastograms clinically are color score and strain ratio. The color score is a qualitative measure estimated by radiologists, and its high subjectiveness may lead to error. Although the strain ratio is a quantitative method, the region selected to calculate the value is subjective and its accuracy is still quite low. A new effective, accurate, and quantitative metric using computer aided diagnosis (CAD) techniques is proposed in this paper. The statistical features and texture features are extracted from the lesion region on the elastogram. The important and reliable features are selected by using Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm. The selected features were input to the SVM to classify the thyroid nodules. The experiment results confirm that the method is more accurate and robust than color score and strain ratio.

Keywords- Thyroid nodule; Elastography; mRM; SVM

I. INTRODUCTION Elastography is a newly developed dynamic technique

that uses US to provide an estimation of tissue stiffness by measuring the degree of distortion under an external force. This technique is currently under clinical investigation for many applications and has shown promising results, such as breast [1, 2], thyroid [3], and prostate [4, 5].

At present, there are two commonly used methods to evaluate the color elastogram for thyroid nodule diagnosis. The first one is a 4-pattern criterion [6]: for malignant case, the whole nodule area is stiff and mainly rendered with blue; and for benign case, the nodule area is mainly soft and rendered with green. The radiologist assigned score according to the color pattern in the lesion region; the higher score indicates the higher possibility of being malignant lesion, and the lower score indicates the higher possibility of being benign lesion. The method is qualitative and subjective. The second one is a semi-quantitative method [7]. It is a ratio calculated by computing the mean strain of the surrounding normal tissue and the mean strain of the lesion using radio frequency (RF) data. The radiologist delineated the lesion region and

surrounding normal tissue region at the same depth on the elastogram separately, and then the mean elasticities in the normal tissue region and the legion were calculated, and their ratio is the strain ratio. The larger strain ratio suggests that the lesion is harder and it indicates higher possibility of being malignant legion. The method attempts to evaluate color elastogram quantitatively, but radiologists have to select the lesion and surrounding normal tissue manually and subjectively, and it may lead to error.

To solve the above problem, this paper proposed a novel effective and robust metric. First, the real elasticity information is extracted. Then statistical and texture features of the lesion were extracted. A reliable and effective subset of the features was selected by minimal-redundancy-maximal-relevance (mRMR) algorithm [8]. Finally, a SVM classifier is used to classify the nodules into malignant and benign. The experimental results show that the metric proposed by this paper has higher accuracy and robustness than that of the color score and strain ration.

II. MATERIALS AND METHODS

A. Patients and imaging acquisition 125 patients (98 female, mean age 46.31±9.79 years;

range 11~67 years; and 27 male, mean age 54.9±11.7 years, range 29~81 years) were evaluated in the study. The mean size of the nodules was 1.74cm (range 0.77~2.64cm). The final pathological results of the nodules are 56 malignant and 69 benign.

Among malignant nodules, there were 44 papillary carcinomas, 7 microcarcinomas and 4 microcarcinomas coexisted with nodular goiters, and 1 medullar carcinoma. Among benign nodules, there were 56 nodular goiters and 13 adenomas.

Both the conventional Ultrasonography (US) and the real-time Elastography were performed with the HITACHI Vision 900 system (Hitachi Medical System, Tokyo, Japan)

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Figure 1. Full scale of the hue component

(a) (b)

Figure 2. (a) color elastography (b) lesion drawn by radiologist

equipped with a liner probe with central frequency of 6~13MHz.

All the examinations were conducted and recorded by two experienced sonographers who were blind to the history and pathologic results. Both of them have more than 6 years’ experience in scanning and about 1 month special training in acquiring elastograms.

B. Elasticity information extraction To extract elasticity information, the original color

elastogram is transformed from RGB color space to HSV color space.

Because the elasticity information was encoded with color; for decoding it, only the color information is needed. Therefore, the hue component in HSV color space is extracted to represent the elasticity value. The (1) are utilized to compute the hue value from RGB space.

0

0

0

0

0

0

60

60 2

60 2

60 4

60 4

60 6

G Bif R G B HR B

R Bif G R B HG BB Rif G B R HG RG Rif B G R HB RR Gif B R G HB GB Gif R B G HR G

−⎧ ≥ ≥ = ×⎪ −⎪−⎛ ⎞⎪ > ≥ = × −⎜ ⎟⎪ −⎝ ⎠⎪−⎛ ⎞⎪ ≥ > = × +⎜ ⎟⎪ −⎝ ⎠⎪

⎨ −⎛ ⎞⎪ > > = × −⎜ ⎟⎪ −⎝ ⎠⎪ −⎛ ⎞⎪ > ≥ = × +⎜ ⎟−⎝ ⎠

−⎛ ⎞≥ > = × −⎜ ⎟−⎝ ⎠⎩

⎪⎪⎪⎪

(1)

The hue value and its corresponding color are illustrated [9] (Fig. 1). The hue value from 00 to 3600, the corresponding color is from red, green, and blue to red. The elasticity magnitude from soft to hard is encoded to color from red to blue. The variable used to reflect the elasticity information should monotonically increase or decrease. For the hue from red to blue, it is monotonically increase, but according to (1), if pixel has R B G≥ > , the color of pixel is redder than blue or green, and the hue value will be in 3000 to 3600, this will break the rule of monotonically increasing. The problem was corrected in this paper by setting the hue value between 300-3600 to the lowest hue value 00, i.e., red pixel which means soft will have low hue value consistently, and the hue value will be monotonically increasing from 00 to 3000.

C. Elastographic feature extraction In this paper, the representative static images were

selected manually from the dynamic elastogram sequences. The lesion regions on the B-mode images were drawn manually by an experienced radiologist (Fig. 2). The contours of the lesions were mapped to the corresponding elastograms automatically, and the features were extracted from the corresponding lesions on elastogram. The first order statistical

features were extracted to describe the elasticity histogram properties, and the second order statistical features were extracted to describe the spatial distribution of the elasticity. If the hue component is normalized to [0,255] intensity level, according to Fig. 1, the pixels whose hue values larger than 128 in lesion are considered as hard, the sum of these pixels is used to define the hard region area in the lesion. The hard region area divided by the lesion area is defined as the hard area ratio (2).

hard area in lesion regionhard area ratiolesion region area

=

(2) The first order statistical features of the lesion region on

the elastogram measure the global elasticity distribution. The features used in this paper are listed in TABLE I.

In the table, i is the elasticity magnitude of the pixel. The hue value of the pixel is normalized to [0, 255] to represent the elasticity magnitude. L is the largest elasticity magnitude in lesion region, and P(i) is the probability of the i-th elasticity magnitude in lesion region.

In this paper, 16 co-occurrence matrices along 4 directions ( )0 0 0 00 , 45 ,90 ,135θ = and with 4 distances

( )1,2,3, 4d = are constructed. For reducing computational complexity and preserving details, according to [10], the number of hue values for calculating the co-occurrence matrices is 64. Four features are extracted from the co-occurrence matrix: contrast, correlation, energy, and homogeneity [11]. The above features of the same distance are averaged to reduce the dimension of feature vectors. Finally, there are 16 features extracted from the co-occurrence matrix.

TABLE I. First order statistical features

Feature Formula F1: Mean ( )

1

0

L

iiP iμ

−

==∑

F2: Mode i, which occur most in the lesion region F3: Variance

( ) ( )1

22

0

L

ii P iσ μ

−

=

= −∑

F4: Skewness ( ) ( )

13

30

L

im i P iμ

−

== −∑

F5: Kurtosis ( ) ( )

14

40

L

i

m i P iμ−

=

= −∑

F6: Entropy ( ) ( )

1

20

logL

i

H P i P i−

=

= −∑

F7: Energy ( )

1 2

0

L

i

E P i−

=

= ⎡ ⎤⎣ ⎦∑

F8: Smoothness 2

111

Rσ

= −+

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D. Feature selection and thyroid nodule classification There are totally 25 features extracted from a lesion. It is

too computationally expensive to include all features, and there may be dependency and redundancy among them. Selecting an optimal feature subset is needed.

The mRMR (minimum redundancy maximum relevance) method [12] was used in this paper for feature selection. Top 5 features are selected and their combinations are used for experiments, finally, the optimal 2 features are selected according to experiment results.

SVM is selected for classifying thyroid nodules into benign and malignant. The training samples are mapped to higher dimension space with a kernel function, and an optimal decision plane can be created [13]. There are two advantages of SVM: the generalization ability is optimal by maximizing the margin distance, and it can solve nonlinear classification tasks by mapping samples to higher dimension space [14].

III. RESULTS

A. Feature combination The performance of the proposed feature extraction and

classification strategy is evaluated by the classification accuracy. Define the number of correctly and incorrectly classified malignant nodules as true positive (TP) and false negative (FN), and the number of correctly and incorrectly classified benign nodules as true negative (TN) and false positive (FP), respectively; the classification accuracy is defined as: (TP+TN)/(TP+TN+FP+FN). Combinations of top 5 features and their corresponding classification performances are listed in TABLE II.

The experimental results confirm that using all of features to classify the samples may not get good performance, and the features having less relevance with the target class should be removed. The results show that top 1 feature (the hard area ratio) produces the best discrimination power among all features and it is very sensitive to the malignant cases; therefore, it can be viewed as the quantitative criterion for the color pattern score method. The combination of top 1 and 2 features gets the best performance. Top 2 feature is the energy property got from the co-occurrence matrix with distance 3. It means that the spatial distribution of elasticity is helpful to distinguish malignant nodules from benign nodules.

TABLE II. Classification result using feature combination

Feature Combination TP FN TN FP Accuracy (%)

All features 47 9 63 6 88 Top 5 features 52 4 63 6 92 Top 4 features 52 4 64 5 92.8 Top 3 features 52 4 64 5 92.8 Top 2 features 53 3 64 5 93.6 Top 1 feature 52 4 63 6 92

Combine top 2,3,4,5 feature 45 11 63 6 86.4

B. Compare with the strain ratio method The strain ratio was defined for comparing the mean

elasticity in the surrounding normal tissues with the mean elasticity in the lesion region. The radiologist drew the lesion and surrounding normal tissue region on the elastogram and the machine calculated the strain ratio. For eliminating operation error, the strain ratios of the three images of the same case were averaged as the value of this case. The value was used to classify the nodules into malignant and benign, and the performance was compared with that of our method; the classification accuracy is 93.6% using our method and 87.2% using strain ratio (TABLE III).

The result shows that the proposed method has higher accuracy than that of the strain ratio method. Due to the error introduced by the selection of the lesion and normal tissue when using the strain ratio method.

C. Compare with the color score method For comparing with the color score method, three

radiologists who were blind to the pathological results were invited to analyze the cases independently after the elastrograms were stored. All of them have more than 6 years’ experience of thyroid US examination. Each radiologist was provided with a sheet containing the 4-patten classification criterion. They were asked to evaluate the elastograms and score for every lesion according to the color distribution of the nodules.

The score was used to classify the nodules into malignant and benign, and the performance was compared with that of our method; the classification accuracy is 93.6% using our method and 83.2% using color score (TABLE III).

The results confirm that the features proposed in this paper have more discrimination power than that of the color score method.

D. Test the robustness to the delineation In experiment, the lesion was outlined manually by the

radiologist. For future research, automated segmentation is needed; therefore, testing whether the method is robust to the segmentation is very meaningful.

For testing the robustness, the lesion drawn by the radiologist was enlarged or shrunk according to the radius, and the method tests on the new lesion region. The result is listing in Table IV.

The results show that when the lesion dilated 1% to 10% of the radius, the maximum change rate of the accuracy is 2.5%; and the average change rate of the accuracy is 1.7%; shrink 1% to 10% of the radius, the maximum change rate of the accuracy is 1.7%; the average change rate of the accuracy is 0.5%. It reveals that the method is not sensitive to the delineation. The robustness can eliminate the effect of the delineations by different radiologists. It can tolerate error when it is auto-segmented by the computer.

TABLE III. Compare with strain ratio and color score Feature TP FN TN FP Accuracy (%)

Selected Features 53 3 64 5 93.6 Strain Ratio 44 12 65 4 87.2 Color Score 44 12 60 9 83.2

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TABLE IV. Sensitivity to delineation

Lesion Region TP FN TN FP Accuracy (%)

Normal 53 3 64 5 93.6 Dilate by 1% radius 53 3 64 5 93.6 Dilate by 2% radius 53 3 64 5 93.6 Dilate by 3% radius 52 4 63 6 92 Dilate by 4% radius 52 4 64 5 92.8 Dilate by 5% radius 51 5 63 6 91.2 Dilate by 6% radius 51 5 63 6 91.2 Dilate by 7% radius 51 5 63 6 91.2 Dilate by 8% radius 51 5 63 6 91.2 Dilate by 9% radius 51 5 63 6 91.2

Dilate by 10% radius 51 5 63 6 91.2 Shrink by 1% radius 52 4 64 5 92.8 Shrink by 2% radius 51 5 64 5 92 Shrink by 3% radius 52 4 64 5 92.8 Shrink by 4% radius 53 3 64 5 93.6 Shrink by 5% radius 52 4 64 5 92.8 Shrink by 6% radius 53 3 64 5 93.6 Shrink by 7% radius 52 4 64 5 92.8 Shrink by 8% radius 53 3 64 5 93.6 Shrink by 9% radius 53 3 64 4 94.4

Shrink by 10% radius 52 4 64 5 92.8

IV. DISCUSSION The real-time elastography is a newly developed medical

imaging technique which measures the tissue’s biomechanics properties. This technique is currently under clinical investigation for many applications. But the evaluation criteria of elastography such as color score and strain ratio are subjective. It is highly dependent on the radiologist’s experience. A reliable CAD method to quantitatively analyze the tissue’s biomechanics properties is needed.

The method proposed in this paper is a new quantitative measurement of elastogram. The experiment results show that it is very predictive and it is also robust and immune to the delineations by radiologists. The proposed metric is objective and has higher classification accuracy than that of the color score and strain ratio. The experiment results also show that the proposed metric is insensitive to the delineations. Therefore, the automatic segmentation and classification of the nodules could be very promising.

There are two limitations in this study. First, we just concentrated on solid nodules, regardless the cystic and mixed nodules. It was reported [15] that most of the cystic and predominant cystic nodules are benign, and the cystic part demonstrated BGR (blue-green-red) phenomenon in elastograms. Second, the static images and lesions were selected and delineated manually; the automatic selection and segmentation will be studied in future research.

ACKNOWLEDGMENT This work was supported in part by National Science

Foundation of China; Grant No. 60873142; and the Innovation Foundation of Harbin (2009RFXXS211)

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