Viet Dzung Nguyen and Duc Thuan Nguyen and Tien Dzung Nguyen and Van Thanh Pham
An Automated Method to Segment and Classify Masses in Mammograms
25 - 30
2009
3
4
International Journal of Biomedical and Biological Engineering
https://publications.waset.org/pdf/5514
https://publications.waset.org/vol/28
World Academy of Science, Engineering and Technology
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 ComputerAided 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 RegionsOf
Interest detection, feature extraction and classification. Our CAD
system was evaluated on MiniMIAS database consisting 322
digitalized mammograms. The CAD systems 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.
Open Science Index 28, 2009