Pre-CAD system for normal mammogram detection using local binary pattern features

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. We aim here to improve CAD system's performance by adding a preprocessing step based on the density of the breast to reduce the false negative rate significantly. Mammograms are divided into two distinct categories according to breast density (fatty, and dense). Three LBP-based features are extracted for each of dense and fatty mammograms. A one-class classifier is used for each tissue-type separately to enhance the performance of the overall classification task. The sensitivity for each tissue type was improved significantly when used separately compared to the sensitivity of existing systems that uses all mammograms regardless of tissue type.