Support vector machine for breast MR image classification

Authors:
Chien-Shun Lo;Chuin-Mu Wang
Affiliations:
Department of Multimedia Design, National Formosa University, 64, Wunhua Rd, Huwei Township, Yunlin, 63201, Taiwan;Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, 411, Taiwan
Venue:
Computers & Mathematics with Applications
Year:
2012

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Abstract

MR images have been used extensively in clinical trials in recent years because they are harmless to the human body and can obtain detailed information by scanning the same slice with various frequencies and parameters. In this paper, we want to detect the breast tissues within multi-spectral MR images. In the image classification, we apply a support vector machine (SVM) to breast multi-spectral magnetic resonance images to classify the tissues of the breast. In order to verify the feasibility and efficiency of this method, evaluations using classification rate and likelihood ratios are adopted based on manifold assessment and a series of experiments are conducted and compared with the commonly used C-means (CM) for performance evaluation. The results show that the SVM method is a promising and effective spectral technique for MR image classification.