COMPUTER AIDED SYSTEM FOR BREAST CANCER DIAGNOSIS USING CURVELET TRANSFORM

Breast cancer is a leading cause of death among women worldwide. Early detection is the key for improving breast cancer prognosis. Digital mammography remains one of the most suitable tools for early detection of breast cancer. Hence, there are strong needs for the development of computer aided diagnosis (CAD) systems which have the capability to help radiologists in decision making. The main goal is to increase the diagnostic accuracy rate. In this thesis we developed a computer aided system for the diagnosis and detection of breast cancer using curvelet transform. Curvelet is a multiscale transform which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing edges in images. We started this study by developing a diagnosis system. Five feature extraction methods were developed with curvelet and wavelet coefficients to differentiate between different breast cancer classes. The results with curvelet and wavelet were compared. The experimental results show a high performance of the proposed methods and classification accuracy rate achieved 97.30%. The thesis then provides an automatic system for breast cancer detection. An automatic thresholding algorithm was used to separate the area composed of the breast and the pectoral muscle from the background of the image. Subsequently, a region growing algorithm was used to locate the pectoral muscle and suppress it from the breast. Then, the work concentrates on the segmentation of region of interest (ROI). Two methods are suggested to accomplish the segmentation stage: an adaptive thresholding method and a pattern matching method. Once the ROI has been identified, an automatic cropping is performed to extract it from the original mammogram. Subsequently, the suggested feature extraction methods were applied to the segmented ROIs. Finally, the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were used to determine whether the region is abnormal or normal. At this level, the study focuses on two abnormality types (mammographic masses and architectural distortion). Experimental results show that the introduced methods have very high detection accuracies. The effectiveness of the proposed methods has been tested with Mammographic Image Analysis Society (MIAS) dataset. Throughout the thesis all proposed methods and algorithms have been applied with both curvelet and wavelet for comparison and statistical tests were also performed. The overall results show that curvelet transform performs better than wavelet and the difference is statistically significant

Automatic Segmentation And Detection Of Mass In Digital Mammograms

This paper presents an automated system for mass segmentation and detection in mammograms. Initially, breast segmentation is applied to separate the breast and non-breast area. Then, image enhancement is employed to improve the contrast of the tissues structure in mammograms. Finally, constraint region growing based on local statistical texture analysis is applied to detect and segment out the mass from the mammograms

Automatic Segmentation And Detection Of Mass In Digital

This paper presents an automated system for mass segmentation and detection in mammograms

Detection of breast pathologies in digital mammography images by thresholding and mathematical morphology

This paper proposes an algorithm for mass and micro-calcification detection by manual thresholding and prewitt detector. This algorithm has been tested using mammography images of different densities from multiple databases of a health clinic and images taken from the internet (40 images in total). The results are very accurate, allowing better detection of breast pathologies (mass and micro-calcification). Finally, the detection of breast pathologies was performed using as input a detection algorithm specially designed for this purpose. After segmentation by manual thresholding, morphological opening, morphological dilatation and Prewitt contour detection we have a demarcation of the masses and breast micro-calcification. The results obtained show the robustness of the proposed manual thresholding method. In order to evaluate the efficiency of our pathology detector, we compared our results with those in the literature and performed a qualitative evaluation with a rate of 98.04% for the detection of breast pathologies.  A radiologist from the health clinic evaluated the results and considers them acceptable to the CAD

Computer Aided Diagnosis - Medical Image Analysis Techniques

Breast cancer is the second leading cause of death among women worldwide. Mammography is the basic tool available for screening to find the abnormality at the earliest. It is shown to be effective in reducing mortality rates caused by breast cancer. Mammograms produced by low radiation X-ray are difficult to interpret, especially in screening context. The sensitivity of screening depends on image quality and unclear evidence available in the image. The radiologists find it difficult to interpret the digital mammography; hence, computer-aided diagnosis (CAD) technology helps to improve the performance of radiologists by increasing sensitivity rate in a cost-effective way. Current research is focused toward the designing and development of medical imaging and analysis system by using digital image processing tools and the techniques of artificial intelligence, which can detect the abnormality features, classify them, and provide visual proofs to the radiologists. The computer-based techniques are more suitable for detection of mass in mammography, feature extraction, and classification. The proposed CAD system addresses the several steps such as preprocessing, segmentation, feature extraction, and classification. Though commercial CAD systems are available, identification of subtle signs for breast cancer detection and classification remains difficult. The proposed system presents some advanced techniques in medical imaging to overcome these difficulties

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Segmentation and Feature Extraction of Tumors from Digital Mammograms

Mammography is one of the available techniques for the early detection of masses or abnormalities which is related to breast cancer. Breast Cancer is the uncontrolled of cells in the breast region, which may affect the other parts of the body. The most common abnormalities that might indicate breast cancer are masses and calcifications. Masses appear in a mammogram as fine, granular clusters and also masses will not have sharp boundaries, so often difficult to identify in a raw mammogram. Digital Mammography is one of the best available technologies currently being used for the early detection of breast cancer. Computer Aided Detection System has to be developed for the detection of masses and calcifications in Digital Mammogram, which acts as a secondary tool for the radiologists for diagnosing the breast cancer. In this paper, we have proposed a secondary tool for the radiologists that help them in the segmentation and feature extraction process. Keywords: Mammography, Breast Cancer, Masses, Calcification, Digital Mammography, Computer Aided Detection System, Segmentation, Feature Extractio