SVM Based System for classification of Microcalcifications in Digital Mammograms

Abstract

This paper presents a SVM based computer-aided diagnosis (CAD) system for the characterization of clustered microcalcifications in digitized mammograms. First, the region of interest (ROI) in mammogram is enhanced using morphological enhancement (MORPHEN) method. Second, pixels in potential microcalcification regions are segmented out by using edge detection and morphological operations. Third, features based on shape, texture and statistical properties are extracted from each region. Finally, these features are fed to a SVM based classifier for identifying the clusters as either benign or malignant. The SVM with RBF kernel gave A(z)=0.9803 with 97% accuracy and the SVM with polynomial kernel gave A(z)=0.9541 with 95% accuracy.