A New Approach for Clustered Microcalcifications Detection

Abstract

Clustered microcalcifications (MCs) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is an important problem in computer aided detection. To improve the performance of detection, we propose a bagging-based twin support vector machine (B-TWSVM) to detect MCs. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed by using a simple artifact removal filter and a well designed high-pass filter. Then the combined image feature extractors are employed to extract 164 image features. In the combined image feature space, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained B-TWSVM is used as a classifier to make decision for the presence of MCs or not. A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithms. The results of this study indicate the potential of proposed approach for computer-aided detection of MCs.