Abstract
Breast tissue density is one of the symptoms for breast cancer detection. Fully automatic breast tissue density classification is presented in this work. Present work consists of four steps which include breast region extraction and enhancement of mammograms, segmentation, feature extraction, and breast tissue density classification. Enhancement of mammogram is done by applying fractional order differential based filter. Segmentation of breast tissue segmentation has been done by using clustering based fast fuzzy c-means technique. Further, texture based local binary pattern (LBP) and dominant rotated local binary pattern (DRLBP) features have been computed from the extracted breast tissues to characterize its texture property. Support vector machine with linear kernel functions are used to classify the breast tissue density. Proposed algorithm is validated on the publicly available 322 mammograms of Mini-Mammographic Image Analysis Society (MIAS).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
