Abstract
This paper presents a novel approach to microcalcification clusters (MCs) detection in mammograms based on the tensor subspace learning and twin support vector machines (TWSVMs). The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is enhanced by using a simple artifact removal filter and a well designed high-pass filter. Then the tensor subspace learning algorithms, tensor subspace analysis (TSA) and general tensor discriminant Analysis (GTDA), are employed to extract subspace features. In subspace feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and 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 experimental results illustrate its effectiveness.
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