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

Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective

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

Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

This paper presents a method for automatic breast pectoral muscle segmentation in mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired by the Holistically-nested Edge Detection (HED) network. Most of the existing methods in the literature are based on hand-crafted models such as straight-line, curve-based techniques or a combination of both. Unfortunately, such models are insufficient when dealing with complex shape variations of the pectoral muscle boundary and when the boundary is unclear due to overlapping breast tissue. To compensate for these issues, we propose a neural network framework that incorporates multi-scale and multi-level learning, capable of learning complex hierarchical features to resolve spatial ambiguity in estimating the pectoral muscle boundary. For this purpose, we modified the HED network architecture to specifically find ‘contour-like’ objects in mammograms. The proposed framework produced a probability map that can be used to estimate the initial pectoral muscle boundary. Subsequently, we process these maps by extracting morphological properties to find the actual pectoral muscle boundary. Finally, we developed two different post-processing steps to find the actual pectoral muscle boundary. Quantitative evaluation results show that the proposed method is comparable with alternative state-of-the-art methods producing on average values of 94.8 ± 8.5% and 97.5 ± 6.3% for the Jaccard and Dice similarity metrics, respectively, across four different databases