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

Authors:
Xinsheng Zhang;Xinbo Gao
Affiliations:
Xi'an University of Architecture and Technology, No. 13, YanTa Road, Xi'an, Shaan xi 710055, China;Xidian University, No.2, South Taibai Road, Xi'an, Shaanxi 710071, China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

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Abstract

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.