Microcalcification detection system in digital mammogram using two-layer SVM

Microcalcification detection in a mammogram is an effective method to find the early stage of breast tumor. Especially, computer aided diagnosis (CAD) improves the working performance of radiologists and doctors as it offers an efficient microcalcification detection. In this paper, we propose a microcalcification detection system which consists of three modules; coarse detection, clustering, and fine detection module. The coarse detection module finds candidate pixels from an entire mammogram which are suspected as a part of a microcalcification. The module not only extracts two median contrast features and two contrast-to-noise ratio features, but also categorizes the candidate pixels with a linear kernel-based SVM classifier. Then, the clustering module forms the candidate pixels into regions of interest (ROI) using a region growing algorithm. The objective of the fine detection module is to decide whether the corresponding region classifies as a microcalcification or not. Eleven features including distribution, variance, gradient, and various edge components are extracted from the clustered ROIs and are fed into a radial basis function-based SVM classifier to determine the microcalcification. In order to verify the effectiveness of the proposed microcalcification detection system, the experiments are performed with full-field digital mammogram (FFDM). We also compare its detection performance with an ANN-based detection system.