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

Computer-Aided Breast Cancer Diagnosis

This research advances computer-aided breast cancer diagnosis. More than 50 million women over the age of 40 are currently at risk from this disease in the United States. Computer-aided diagnosis is offered as a second opinion to radiologists to aid in decreasing the number of false readings of mammograms. This automated tool is designed to enhance detection and classification. New feature extraction methods are presented that provide increased classification power. Angular second moment, a second-order gray-level histogram statistic, provides baseline accuracy. Two novel extraction methods, eigenmass and wavelets, are introduced to the field. Based on the Karhunen-Loeve Transform, eigenmass features are developed using eigenvectors to alter the data set into new coefficients. Wavelets, previously only exploited for their segmentation benefits, are explored as features for classification. Daubechies-4, Danbechies-2O, and biorthogonal wavelets are each investigated. Applied to 94 difficult-to-diagnosis digitized microcalcification cases, performance is 74 percent correct classifications. Feature selection techniques are presented which further improve performance. Statistical analysis, neural and decision boundary-based methods are implemented, compared, and validated to isolate and remove useless features. The contribution from this analysis is an increase to 88 percent correct classification in system performance

Focal Spot, Summer 1989

https://digitalcommons.wustl.edu/focal_spot_archives/1052/thumbnail.jp

Clinical study in phase- contrast mammography: image-quality analysis

The first clinical study of phase-contrast mammography (PCM) with synchrotron radiation was carried out at the Synchrotron Radiation for Medical Physics beamline of the Elettra synchrotron radiation facility in Trieste (Italy) in 2006–2009. The study involved 71 patients with unresolved breast abnormalities after conventional digital mammography and ultrasonography exams carried out at the Radiology Department of Trieste University Hospital. These cases were referred for mammography at the synchrotron radiation facility, with images acquired using a propagation-based phase-contrast imaging technique. To investigate the contribution of phase-contrast effects to the image quality, two experienced radiologists specialized in mammography assessed the visibility of breast abnormalities and of breast glandular structures. The images acquired at the hospital and at the synchrotron radiation facility were compared and graded according to a relative seven-grade visual scoring system. The statistical analysis highlighted that PCM with synchrotron radiation depicts normal structures and abnormal findings with higher image quality with respect to conventional digital mammography

Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

Focal Spot, Spring 1982

https://digitalcommons.wustl.edu/focal_spot_archives/1031/thumbnail.jp