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\iteman{io-port 06105328}
\itemau{Liu, Xiaoming; Li, Bo; Liu, Jun; Xu, Xin; Feng, Zhilin}
\itemti{Mass diagnosis in mammography with mutual information based feature selection and support vector machine.}
\itemso{Huang, De-Shuang (ed.) et al., Intelligent computing theories and applications. 8th international conference, ICIC 2012, Huangshan, China, July 25--29, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-31575-6/pbk). Lecture Notes in Computer Science 7390. Lecture Notes in Artificial Intelligence, 1-8 (2012).}
\itemab
Summary: 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.
\itemrv{~}
\itemcc{}
\itemut{Mass diagnosis; Mammography; Mutual information; feature selection; Support vector machine}
\itemli{doi:10.1007/978-3-642-31576-3\_1}
\end