Mass diagnosis in mammography with mutual information based feature selection and support vector machine

Authors:
Xiaoming Liu;Bo Li;Jun Liu;Xin Xu;Zhilin Feng
Affiliations:
College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China;Zhijiang College, Zhejiang University of Technology, Hangzhou, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

Mass classification is an important problem in breast cancer diagnosis. In this paper, we investigated the classification of masses with feature selection. Based on the initial contour guided by radiologist, level set algorithm is used to deform the contour and achieves the final segmentation. Morphological features are extracted from the boundary of segmented regions. Then, important features are extracted based on mutual information criterion. Linear discriminant analysis and support vector machine are investigated for the final classification. Mammography images from DDSM were used for experiment. The method achieved an accuracy of 86.6% with mutual information based feature selection and SVM classifier. The experimental result shows that mutual information based feature selection is useful for the diagnosis of masses.