Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.081134
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 11, 2017.
Abstract: According to the World Health Organization, breast cancer is the main cause of cancer death among women in the world. Until now, there are no effective ways of preventing this disease. Thus, early screening and detection is the most effective method for rising treatment success rates and reducing death rates due to breast cancer. Mammography is still the most used as a diagnostic and screening tool for early breast cancer detection. In this work, we propose a method to segment and classify masses using the regions of interest of mammographic images. Mass segmentation is performed using a fuzzy active contour model obtained by combining Fuzzy C-Means and the Chan-Vese model. Shape and margin features are then extracted from the segmented masses and used to classify them as benign or malignant. The generated features are usually imprecise and reflect an uncertain representation. Thus, we propose to analyze them by a possibility theory to deal with imprecise and uncertain aspect. The experimental results on Regions Of Interest (ROIs) extracted from MIAS database indicate that the proposed method yields good mass segmentation and classification results.
Marwa Hmida, Kamel Hamrouni, Basel Solaiman and Sana Boussetta, “An Efficient Method for Breast Mass Segmentation and Classification in Mammographic Images” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081134