A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine.

Affiliation

Ketabi H(1), Ekhlasi A(2), Ahmadi H(3).
Author information:
(1)Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran.
(2)Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran.
(3)Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran. [Email]

Abstract

Breast cancer continues to be a widespread health concern all over the world. Mammography is an important method in the early detection of breast abnormalities. In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion. In this study, a fully process-integrated approach with developing a CAD system is presented for the detection of breast masses based on texture description, spectral clustering, and Support Vector Machine (SVM). To this end, breast Regions of Interest (ROIs) are automatically detected from digital mammograms via gray-scale enhancement and data cleansing. The ROIs are segmented as labeled multi-sectional patterns using spectral clustering by the means of intensity descriptors relying on the region's histogram and texture descriptors based on the Gray Level Co-occurrence Matrix (GLCM). In the next step, shape and probabilistic features are derived from the segmented sections and given to the Genetic Algorithm (GA) to do the feature selection. The optimal feature vector comprising a fusion of selected shape and probabilistic features is submitted to linear kernel SVM for robust and reliable classification of mass tissues from the non-mass. Linear discrimination analysis (LDA) is also performed to ascertain the significance of the nominated feature space. The classification results of the proposed approach are presented by sensitivity, specificity, and accuracy measures, which are 89.5%, 91.2%, and 90%, respectively.