@article{(Open Science Index):https://publications.waset.org/pdf/5514, title = {An Automated Method to Segment and Classify Masses in Mammograms}, author = {Viet Dzung Nguyen and Duc Thuan Nguyen and Tien Dzung Nguyen and Van Thanh Pham}, country = {}, institution = {}, abstract = {Mammography is the most effective procedure for an early diagnosis of the breast cancer. Nowadays, people are trying to find a way or method to support as much as possible to the radiologists in diagnosis process. The most popular way is now being developed is using Computer-Aided Detection (CAD) system to process the digital mammograms and prompt the suspicious region to radiologist. In this paper, an automated CAD system for detection and classification of massive lesions in mammographic images is presented. The system consists of three processing steps: Regions-Of- Interest detection, feature extraction and classification. Our CAD system was evaluated on Mini-MIAS database consisting 322 digitalized mammograms. The CAD system-s performance is evaluated using Receiver Operating Characteristics (ROC) and Freeresponse ROC (FROC) curves. The archived results are 3.47 false positives per image (FPpI) and sensitivity of 85%.}, journal = {International Journal of Biomedical and Biological Engineering}, volume = {3}, number = {4}, year = {2009}, pages = {25 - 30}, ee = {https://publications.waset.org/pdf/5514}, url = {https://publications.waset.org/vol/28}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 28, 2009}, }