id: 06105328
dt: a
an: 06105328
au: Liu, Xiaoming; Li, Bo; Liu, Jun; Xu, Xin; Feng, Zhilin
ti: Mass diagnosis in mammography with mutual information based feature
    selection and support vector machine.
so: 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).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: Mass diagnosis; Mammography; Mutual information; feature selection; Support
    vector machine
ci: 
li: doi:10.1007/978-3-642-31576-3_1
ab: 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.
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