final report-draft edited

7/31/2019 Final Report-draft Edited

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1. INTRODUCTION

1.1 Infrared Thermography

Infrared (IR) thermography is a branch of imaging science that detects

electromagnetic radiation emitted by objects in infrared range and generates the

corresponding radiation images, called thermograms. As all objects with temperature above

absolute zero, or -273.15 degree Celsius (C), emit infrared radiation from their surface

(Bronzino, 2005). There are actually two types of infrared imaging system, namely active

infrared and passive infrared. Active infrared requires targets to be illuminated by near

infrared beams and the reflected radiation is measured. However, active infrared system is

greatly limited by the distance between infrared source and the target. Meanwhile, passive

system is sensitive towards far infrared wavelengths, or more commonly known as heat

radiation, dissipated by the target itself. Since the thermal imaging systems do not depend

on reflected ambient light, the generated thermal images are entirely ambient light-level

independent.

The amount of radiation emitted by an object increases as its temperature rises.

Using a thermographic imaging system, the variations in infrared radiation emitted by an

object of interest along with its surrounding can be approximated into analogous

temperature values and produces the corresponding thermogram. Stefan-Boltzmann Law

states that the total emissive power of an object is proportional to the emissivity of its

surface and the fourth power of its absolute temperature (Bronzino, 2005).

4 2[ / ]i iE T Watt m = (1.1)


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whereEiis the total emissive power;

iis the emissivity; is the Stefan Boltzman constant;

Tis the absolute temperature of an object.

Wavelengths outside the visible electromagnetic spectrum do not map uniformly

into the colour system adopted by human vision, therefore the significance of colours

become lesser. Thus, infrared cameras often generate monochromatic thermograms as a

mean to reduce the hardware complexity. In other words, different temperatures of objects

are distinguished with different shades of grey level. A high greyscale level indicates a

high temperature object which appears to be a bright object on the infrared image and vice

versa. Contrast seen on the image represents the difference of temperature between the

objects. However, as human eyes only capable of distinguish few dozen shades of grey

(Gonzalez & Woods, 2002), the risk of neglecting vital information contents while

analysing monochromatic images is relatively high. Moreover, many infrared imaging

systems often produce infrared images with low signal-to-noise ratio (SNR) and poor

contrast (Ni, Li, & Xia, 2008), which further impedes the proper investigation of

information contents. Therefore, contrast enhancement becomes an essential key step in

infrared imaging based applications.

1.2Breast Infrared Thermography

Examination of heat pattern and vessels development in breast is the very first

application of infrared imaging in medicine (Qi & Diakides, 2007). This imaging technique

is known as breast thermography.


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The underlying biological principle lies in the development of new blood vessel in

the breast during precancerous state in a process called angiogenesis. Malignant

angiogenesis is a process where cancerous cells induce the development of new blood

vessels in order to enhance the mass transfer of nutrient and waste to the cancerous cells

(Wang et al., 2010). It is found that malignant angiogenesis starts long before the cells turn

cancerous (Bronzino, 2005). There is a rise in infrared radiation due to higher metabolic

activity and blood flow at the lesion site (Wang et al., 2010). Therefore, detection of this

malignant angiogenesis is the key tool for early detection of breast cancer. Malignant

angiogenesis is characterised by a raise in vascular asymmetry and heat observable through

infrared images. In other words, early stage of breast cancer is detectable through

examination of heat pattern change of breast thermogram.

Figure 1.1: Colourised Infrared Image of Breast (Hobbins & Amalu, 2011)

Figure 1.1 shows a comparison of colourised normal breast image with abnormal

breast image. The picture on the left is an infrared image of normal breast. It is

characterised by dark colour that indicates the low-heat/balanced cool area in breast region.

Meanwhile, the right picture shows an image of abnormal breast, typified by high heat-

activity in the left breast due to formation of new blood vessel for cancerous cells survival.

In short, breast thermography is capable of giving an early sign of possible

development of breast cancer up to 10 years in advance of any other imaging tool. Infrared


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imaging technique is emerging to first-line screening tool to both clinical diagnosis and

health-maintaining thanks to significant advancement in infrared sensing technology as

well as improvement in image-processing algorithm (Qi & Diakides, 2007). Furthermore, a

high detection rate of 95% in early stage cancer is achieved with integration of breast

thermography into mammography and clinical examination (Bronzino, 2005).

2. RESEARCH PROBLEM

Most infrared cameras generate monochromatic thermograms that reflect

temperature differences with different shades of grey intensity. Consider an image I(x,y)

that consists ofL discrete grey intensity levels; for a 8-bit greyscale image,L equals to 256,

which means there are a total of 256 different shades to represent the temperatures.

Contrast seen on the image represents the difference of temperature between the objects.

However, as human eyes only capable of distinguish few dozen shades of grey (Gonzalez

& Woods, 2002), the risk of neglecting vital information contents while analysing

monochromatic images is relatively high. Moreover, many infrared imaging systems often

produce infrared images with low signal-to-noise ratio (SNR) and poor contrast (Ni, Li, &

Xia, 2008), which further impedes the proper investigation of information contents.

Therefore, contrast enhancement becomes an essential key step in infrared imaging based

applications.

2.1 Problem Statement

Infrared thermography screening provides a viable solution for early cancer

detection by identifying abnormal heat dissipated due to formation of malignant


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angiogenesis. However, quality of infrared image is greatly degraded by low contrast and

low signal to noise ratio. Furthermore, the relative small heat difference between mutated

cells and normal cells, especially during early formation, may be difficult to differentiate

by inspecting the low contrast raw infrared image (Kennedy & Seely, 2009). Hence, it is

crucial to apply image contrast enhancement to improve the image vision quality of the

infrared image. This allows clinician to easily identify the presence of breast lesion and the

enhancement process can serves as a pre-emptive step to make the infrared image more

suitable for further analysis procedures, such as pattern recognition or to automatically

identify the lesions without human intervention.

3. OBJECTIVES

To develop an improved contrast enhancement algorithm to overcome or mitigate

the limitations of existing methods.

To develop a stand-alone image enhancement algorithm for breast infrared image.

4. HYPOTHESIS

Better contrast enhancement technique will help to magnify the subtle temperature

differences and make them more noticeable; thus ease the effort and improve accuracy of

clinician in identifying early formation of malignant angiogenesis in breast region.

5. LITERATURE REVIEW


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5.1 Image Contrast Enhancement Techniques

Medical images are images that require high-contrast for a right diagnosis. The

most essential aspect of medical image analysis lies in the precise definition of subjects

contours which requires high contrast between objects. (Larrabide, Novotny, Feijo, &

Taroco, 2005). However, infrared imaging tools often generate low-contrast images and

this downside reduces the detectability of small temperature difference present in the image

(Zhan & Wu, 2010). There are two main categories of enhancement techniques namely

spatial domain technique and frequency domain technique. Spatial domain technique deals

with the image pixels while frequency domain technique operates on the Fourier-

transformed of the image (Ahmed & Nordin, 2011). For spatial method, simplicity of

Histogram equalisation contributes to its extensive employment in contrast enhancement.

However, Histogram equalisation suffers from the problem of over-enhancement and

image deterioration. A number of techniques have been used to overcome its annoying

effects. Nevertheless, each technique has its advantages and drawbacks (Bansal & Goyal,

2011).

5.2.1 Histogram Equalisation (HE) Technique

Histogram Equalisation (HE) is one of the fundamental and widely used image

processing techniques for improving the contrast of an image via dynamic range

modification (Gonzalez & Woods, 2002). In HE, the histogram of an image is redistributed

and forced to be uniform by flattening its distribution of intensities according to a desired

transfer function (Andrew, Tescher, & Kruger, 1972).


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Consider a two dimensional image I(x,y) that consists ofL discrete gray intensity

levels; for a 8-bit greyscale image,L equals to 256. The probability density function (PDF),

pdf(k), can be defined as (Yoon, Han, & Hahn, 2009)

( ) ; 0kn

pdf k k Ln

= < , (5.1)

where nk is the number of pixels forkth grey level, while n denotes the total number of

pixels for input image. Using the PDF, the corresponding cumulative distribution function

(CDF), cdf(k), can be defined as

( )0

( ); 0k

ii

cdf k pdf k k L=

=


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(c) (d)

Figure 5.1: Histogram Equalization (HE) Technique. (a) Original Breast Thermogram, (b)

Histogram of Original Breast Thermogram, (c) Enhanced Image after HE, and (d)

Histogram of Enhanced Image after HE.

5.2.2 Advantages and Disadvantages

As shown in Figure 5.1, HE greatly improves the image vision quality of the

original low contrast infrared image by expanding its dynamic range over the entire

available intensity levels; image details previously obscured by the low contrast value are

made perceptually visible. However, to achieve an uniform probability density, a much

higher contrast gain (change of grey level) will be given to peak regions of the histogram

compared to the rest; in short, contrast gain is scaled proportional to the height of the

histogram. Such phenomenon often leads to drastic brightness change of the input image,

causing artefact known as over-equalisation (Chang & Wu, 1998; Bansal & Goyal, 2011).

5.3.1 Brightness Preserving Bi-Histogram Equalization (BBHE) Technique

Brightness Preserving Bi-Histogram Equalization (BBHE) is a variation of HE with

the aim to minimize the over-equalization effect via preserving the original image mean

brightness. This can be achieved by performing mean-separation algorithm (Kim, 1997);


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histogram of image is divided into sub-histograms based on the original image mean value

and equalised independently. The separating point,Xm, can be calculated as

( ){ }1

0

; 0L

mk

X k pdf k k L

=

=

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