Classification of malignant and benign microcalcification using SVM classifier

Breast Cancer is one of the frequent and leading causes of mortality among woman, especially in developed countries. Woman within the age of 40–69 have more risk of breast cancer. Though breast cancer leads to death, early detection of breast cancer can increase the survival rate. Clustered Microcalcification (MC) in mammograms is the major indication for early detection of breast cancer. MC is quiet tiny bits of calcium, and may show up in clusters or in patterns and is associated with extra cell activity in breast tissue. Usually, the extra cell growth is not cancerous, but sometimes tight clusters of microcalcification can indicate early breast cancer. Individual clusters are difficult to detect, hence an intelligent Computer Aided Detection (CAD) will help the radiologists in detecting the MC clusters in an easy and efficient way. In this paper, we present a new classification approach using Support Vector Machines (SVM) for detection of microcalcification clusters in digital mammograms. Classifying data is a common task in machine learning. SVM is a linear classifier which constructs a hyperplane or set of hyperplanes in an infinite dimensional space. The MC detection is formulated as a supervised learning problem and we apply SVM as a classifier to determine at each pixel location in the mammogram if the MC is present or not. To improve the classification rate Law's texture energy measures are taken from the image Region of interest (ROI). Once the features are computed for each ROI, they can be used as input to the SVM classifier. The method was applied to 322 digitized mammographic images from the MIAS database. Results shows that the classification performance of the proposed approach is superior when compared with several other classification approach discussed in the literature.