Twin support vector machines and subspace learning methods for microcalcification clusters detection

This paper presents a novel framework for microcalcification clusters (MCs) detection in mammograms. The proposed framework has three main parts: (1) first, MCs are enhanced by using a simple-but-effective artifact removal filter and a well-designed high-pass filter; (2) thereafter, subspace learning algorithms can be embedded into this framework for subspace (feature) selection of each image block to be handled; and (3) finally, in the resulted subspaces, the MCs detection procedure is formulated as a supervised learning and classification problem, and in this work, the twin support vector machine (TWSVM) is developed in decision-making of MCs detection. A large number of experiments are carried out to evaluate and compare the MCs detection approaches, and the effectiveness of the proposed framework is well demonstrated.