Automated identification of breast cancer using higher order spectra

Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra Automated identification of breast cancer using higher order spectra

SIM UNIVERSITY SCHOOL OF SCIENCE AND TECHNOLOGY AUTOMATED IDENTIFICATION OF BREAST CANCER USING HIGHER ORDER SPECTRA STUDENT : YOGARAJ (Z0605929) SUPERVISOR : DR RAJENDRA ACHARYA U PROJECT CODE : BME499, JAN09/BME/20 A project report submitted to SIM University in partial fulfilment of the requirements for the degree of Bachelor (Hons) in Biomedical Engineering Jan 2009 1 TABLE OF CONTENTS Title Pages ABSTRACT 4 ACKNOWLEDGEMENTS 5 LIST OF FIGURES 6 LIST OF TABLES 7 CHAPTER 1 INTRODUCTION 8 CHAPTER 2 DATA ACQUISITION 16 CHAPTER 3 PREPROCESSING OF IMAGE DATA 17 3.1 Histogram Equalization 17 CHAPTER 4 FEATURE EXTRACTION 18 4.1 Radon Transform 18 4.2 Higher Order Spectra 20 4.2.1 Higher Order Spectra Features 23 CHAPTER 5 CLASSIFIERS AND SOFTWARE USED 25 2 5.1 Support Vector Machine (SVM) 25 5.2 Gaussian Mixture Model (GMM) 28 5.3 MATLAB 30 CHAPTER 6 RESULTS 32 CHAPTER 7 DISCUSSION 36 CHAPTER 8 CONCLUSION 38 CHAPTER 9 CRITICAL REVIEW 9.1 Criteria and Targets 39 9.2 Project Plan 40 9.3 Strengths and Weaknesses 41 9.4 Priorities for Improvement 42 9.5 Reflections 43 REFERENCES 45 APPENDIX Appendix A: Anova Results from website 50 Appendix B: Anova Results, in Excel format 51 Appendix C: Test Data Results, in Excel format 52 Appendix D: Example of HOS programming, in MATLAB format 53 Appendix E: MATLAB Codes 54 ABSTRACT 3 Breast cancer is the second leading cause of death in women. It occurs when cells in the breast begin to grow out of control and invade nearby tissues or spread throughout the body. This project proposes of a comparative approach for classification of the three kinds of mammograms, normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers, the support vector machine (SVM) and the Gaussian mixture model (GMM), for comparison. The aim of this study is to, develop a feasible interpretive software system which will be able to detect and classify breast cancer patients by employing Higher Order Spectra (HOS) and data mining techniques. The main approach of this project is to employ non-linear features of the HOS to detect and classify breast cancer patients. HOS is known to be efficient as it is more suitable for the detection of shapes. The aim of using HOS is to automatically identify and classify the three kinds of mammograms. The project protocol uses 205 subjects, consisting of 80 normal, 75 benign and 50 cancer, breast conditions. ACKNOWLEDGEMENTS 4 I would like to extend my heartfelt gratitude and appreciation to many people who had made this project possible. I would like to thank The Digital Database for Screening Mammography (DDSM) of USA, for providing the source data in this mammographic image analysis. I would like to thank my tutor, Dr. Rajendra, who had given me the opportunity to undertake this project and also for his continuous support, guidance and encouragement. I would also like to express my appreciation to, Dr Lim Teik Cheng, Head of Multimedia Technology and Design, for his talks on, “Introduction to the ENG499, BME499, MTD499 and ICT499 Capstone Projects” and “Briefing on submission of Thesis and Poster Presentation procedure”, and Dr Lim Boon Lum for his talk on, “Introduction to MATLAB Applications for FYP Projects”. These talks guided me through my journey. The facilities at the Bioelectronics and Biomedical Engineering at UniSIM and Ngee Ann Polytechnic were utilized for this work and I gratefully acknowledge them. Special thanks to my family and friends, for letting me carry on my research in peace while they prepared for Deepavali and other important family events. I would also like to thank my colleagues from Republic Polytechnic (RP), for their full support and understanding in covering my duties during my periods of leave. LIST OF FIGURES Pages 5 Figure 1.1: Anatomy of a Breast 9 Figure 1.2: Anatomy of the Breast 9 Figure 1.3: Benign Breast Image 14 Figure 1.4: Tumour on Left Breast 14 Figure 1.5: Tumour on Right Breast 14 Figure 1.6: Classification Block Diagram 15 Figure 2.1: Normal Breast Image 16 Figure 2.2: Benign Breast Image 16 Figure 2.3: Cancer Breast Image 16 Figure 4.1 and 4.2: Schematic Diagram of Radon Transformation 19 Figure 4.3, 4.4 and 4.5: An example of a Radon Transformation 19 Figure 4.5: Bispectrum Diagram 23 Figure 5.1: An example of GUI 31 LIST OF TABLES Pages 6 Table 6.1: Classifier Input Features 32 Table 6.2: SVM Classifier Results 33 Table 6.3: GMM Classifier Results 33 Table 6.4: Accuracy of SVM and GMM classifiers 34 Table 9.1: Criteria/Targets and Achievements 39 CHAPTER 1: INTRODUCTION 7 The human breast is made up of both fatty tissues and glandular milk-producing tissues. The ratio of fatty tissues to glandular tissues varies among individuals. In addition, with the onset of menopause and decrease in estrogens’ levels, the relative amount of fatty tissue increases as the glandular tissue diminishes [12]. The breasts sit on the chest muscles that cover the ribs. Each breast is made of 15 to 20 lobes. Lobes contain many smaller lobules. Lobules contain groups of tiny glands that can produce milk. Milk flows from the lobules through thin tubes called ducts to the nipple. The nipple is in the centre of a dark area of skin called the areola. Fat fills the spaces between the lobules and ducts. The base of the breast overlies the pectoralis major muscle between the second and sixth ribs in the non-ptotic state. The gland is anchored to the pectoralis major fascia by the suspensor ligaments. These ligaments run throughout the breast tissue from the deep fascia beneath the breast and attach to the dermis of the skin. Since they are not taut, they allow for the natural motion of the breast. These ligaments relax with age and time, eventually resulting in breast ptosis. The lower pole of the breast is fuller than the upper pole. The tail of Spence extends obliquely up into the medial wall of the axilla. The breast also overlies the uppermost portion of the rectus abdominis muscle. The nipple lies above the inframammary crease and is usually level with the fourth rib and just lateral to the mid-clavicular line. 8 Figure 1.1: Anatomy of a Breast The breasts also contain lymph vessels. These vessels lead to small, round organs called lymph nodes. Groups of lymph nodes are near the breast in the axilla (underarm), above the collarbone, in the chest behind the breastbone, and in many other parts of the body. The lymph nodes trap bacteria, cancer cells, or other harmful substances [11]. Figure 1.2: Anatomy of the Breast 9 Breast cancer is a cancer that starts in the breast, usually in the inner lining of the milk ducts or lobules. There are different types of breast cancer, with different stages, aggressiveness, and genetic make-up. While the majority of new breast cancers are diagnosed as a result of an abnormality seen on a mammogram, a lump or change in consistency of the breast tissue can also be a warning sign of the disease. Research has yielded much information about the causes of breast cancers, and it is now believed that genetic and/or hormonal factors are the primary risk factors for breast cancer. Staging systems have been developed to allow doctors to characterize the extent to which a particular cancer has spread and to make decisions concerning treatment options. Breast cancer treatment depends upon many factors, including the type of cancer and the extent to which it has spread. Some types of breast cancers require the hormones estrogens’ and progesterone to grow and have receptors for those hormones. Those types of cancers are treated with drugs that interfere with those hormones and with drugs that shut off the production of estrogens’ in the ovaries or elsewhere. This may damage the ovaries and end fertility [11]. The most common types of breast cancer begin either in the breast's milk ducts (ductal carcinoma) or in the milk-producing glands (lobular carcinoma). The point of origin is determined by the appearance of the cancer cells under a microscope. In situ (non-invasive) breast cancer refers to cancer in which the cells have remained within their place of origin, which means they haven't spread to breast tissue around the duct or lobule. The most common type of non-invasive breast cancer is ductal carcinoma in situ (DCIS), which is confined to the lining of the milk ducts. The abnormal cells haven't spread through the duct walls into surrounding breast tissue. With appropriate treatment, DCIS has an excellent prognosis [12]. 10 [...]... the fact that, 66 percent of breast cancer victims die from it, is alarming Early detection is still the most effective way of dealing with this situation Because the breast is composed of identical tissues in males and females, breast cancer can also occur in males Incidences of breast cancer in men are approximately 100 times less common than in women, but men with breast cancer are considered to... incidence of breast cancer is increasing worldwide and the disease remains a significant public health problem In the UK, all women between the ages of 50 and 70 are offered mammography, every three years, as part of a national breast screening programme About 385,000 of the 1.2 million women diagnosed with breast cancer each year, occur in Asia These issues, narrow down to the detection of breast cancer. .. kinds of eye diseases [33] HOS is known to be efficient as it is more suitable for the detection of shapes The aim of using HOS is to automatically identify and classify the three kinds of mammogram (normal, benign and cancer) [34] This project proposes of a comparative approach for classification of three kinds of mammogram: normal, benign and cancer The features are extracted from the raw 20 images using. .. this type of cancer, typically, no distinct, firm lump is felt, but rather a fullness or area of thickening occurs Breast cancer is the second leading cause of death in women It occurs when cells in the breast begin to grow out of control and invade nearby tissues or spread throughout the body [11, 12] The cause of the disease is not understood till now and there is almost no immediate hope of prevention... asymmetry between sides can be noted Figure 1.5: There is a small speculated tumour in the middle of the right breast, left side of figure 14 The aim of this study is to develop a feasible interpretive software system which will be able to detect and classify breast cancer patients by employing Higher Order Spectra (HOS), and data mining techniques Two techniques were proposed to diagnose the abnormal... (infiltrating) breast cancers spread outside the membrane that lines a duct or lobule, invading the surrounding tissues The cancer cells can then travel to other parts of your body, such as the lymph nodes Invasive ductal carcinoma (IDC) accounts for about 70 percent of all breast cancers The cancer cells form in the lining of the milk duct, then break through the ductal wall and invade the nearby breast tissues... of the commonly missed signs of breast cancer is architectural distortion Fractal analysis and texture measures for the detection of architectural distortion in screening mammograms taken prior to the detection of breast cancer have been applied Gabor filters, phase portrait modeling, fractal dimension (FD) and texture features for the analysis have been used The classification of the three kinds of. .. classifiers, the SVM and GMM, for comparison The aim of this study is to, develop a feasible interpretive software system which will be able to detect and classify breast cancer patients by employing HOS and data mining techniques The main approach of this project is to employ non-linear features of the HOS to detect and classify breast cancer patients The linear spectral techniques contain only independent... x-ray specially developed for taking images of the breast tissue Two or more mammograms, from different angles, are taken of each breast Mammograms are usually only used for women over the age of 35 In younger women the breast tissue is denser; this makes it difficult to detect any changes on the mammogram [36] Using the mammogram, radiologists can detect the cancer 76 to 94 percent accurately, compared... Extraction SVM and GMM Classifiers Normal Benign Cancer Figure 1.6: Proposed block diagram for classification In this work, I compare the performances of SVM and GMM classifiers for the three kinds of mammogram images 15 CHAPTER 2: DATA ACQUISITION For the purpose of the present work, 205 mammogram images, consisting of 80 normal, 75 benign and 50 cancer breast conditions, have been used from the digital . SIM UNIVERSITY SCHOOL OF SCIENCE AND TECHNOLOGY AUTOMATED IDENTIFICATION OF BREAST CANCER USING HIGHER ORDER SPECTRA STUDENT : YOGARAJ (Z0605929) SUPERVISOR : DR. during my periods of leave. LIST OF FIGURES Pages 5 Figure 1.1: Anatomy of a Breast 9 Figure 1.2: Anatomy of the Breast 9 Figure 1.3: Benign Breast Image 14 Figure 1.4: Tumour on Left Breast 14 Figure. the breastbone, and in many other parts of the body. The lymph nodes trap bacteria, cancer cells, or other harmful substances [11]. Figure 1.2: Anatomy of the Breast 9 Breast cancer is a cancer