Twin support vector machines and subspace learning methods for microcalcification clusters detection |
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| Authors: | Xinsheng Zhang Xinbo Gao |
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| Affiliation: | 1. State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Yunnan 650093, China;2. Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Ministry of Education, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100124, China;1. Department of Physics, Institute of Science, Banaras Hindu University, Varanasi 221005, India;2. Institute of Radio Physics and Electronics, University of Calcutta, Kolkata 700009, India;3. S. K. Mitra Centre for Research in Space Environment, Institute of Radio Physics and Electronics, University of Calcutta, Kolkata 700009, India |
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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. |
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