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Mass Classification in Mammography with Morphological Features and Multiple Kernel Learning

Mass Classification in Mammography with Morphological Features and Multiple Kernel Learning,10.1109/icbbe.2011.5780356,Xiaoming Liu,Jun Liu,Zhilin Fen

Mass Classification in Mammography with Morphological Features and Multiple Kernel Learning  
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Classification of mammographic masses as malignant or benign may assist radiologists in reducing the biopsy rate without increasing false negatives. In this paper, we investigated the classification of masses with level set segmentation and multiple kernel learning. Based on the initial contour guided by radiologist, level set segmentation is used to deform the contour and achieves the final segmentation. Morphological features are extracted from the boundary of segmented regions. Linear discriminant analysis, support vector machine and multiple kernel learning are investigated for the final classification. Mammography images from DDSM were used for experiment. The method based on the level set segmentation and the morphological features achieved an accuracy of 76%. The experimental result shows that level set based segmentation can improve the characterization of masses compared with manually rough segmentation.
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