HMPFIM-B: Hybrid Markov Penalized FCM in Mammograms for Breast Cancer

Mammography is an imaging tool which uses low dose low energy x-ray for early detection of tumours in breast. Currently there are number of image based software applications to assist radiologists for better screening. Segmentation is the best way for reliable diagnosis by reducing false rate. So here, we propose novel segmentation algorithm using fuzzy logic. This new approach uses penalized fuzzy c means clustering in mammographic image to give significant improved performance while screening mammogram. The real-time implementation of this paper can be implemented using hardware and software interface with the mammography systems

Texture descriptors applied to digital mammography

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.Postprint (published version