Texture descriptors applied to digital mammography
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Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.
CitacióMata, C., Freixenet, J., Lladó, X., O. A. Texture descriptors applied to digital mammography. A: Erasmus-Mundus Master in Computer Vision and Robotics. "VIBOT VIVA 2008". Edinburgh: 2008, p. 105-110.