Effect of breast density in selecting features for normal mammogram detection

Breast cancer is the second leading cause of cancer deaths in women in the U.S. Two main problems appear to affect the decision of detecting and diagnosing breast cancer: the accuracy of the CAD systems used, and the radiologists' performance in reading mammograms. The main challenge in designing any CAD system is to maintain a high sensitivity level in detecting the abnormalities as the density of the breast increases. In our work, we introduce a novel idea of having a dual system that will process mammograms differently according to breast tissue density. The sensitivity will be significantly improved while keeping the specificity as high as possible. Mammograms are divided into two distinct categories according to breast density(fatty, and dense). Two main set of features are extracted from both dense and fatty mammograms. A one-class classifier is used for each tissue-density separately to enhance the performance of the overall classification task. Results showed that for each density a specific set of features will perform better than others.