MCs Detection with Combined Image Features and Twin Support Vector Machines

Abstract

Breast cancer is a common form of cancer diagnosed in women. Clustered microcalcifications(MCs) in mammograms is one of the important early sign. Their accurate detection is a key problem in computer aided detection (CDAe). In this paper, a novel approach based on the recently developed machine learning technique - twin support vector machines (TWSVM) to detect MCs in mammograms. 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 high-pass filter. Then the combined image feature extractors are employed to extract 164 image features. In the combined feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained 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 algorithm. Experimental results show that the proposed TWSVM classifier is more advantageous for real-time processing of MCs in mammograms.