Detecting mass and its region in mammograms using mean shift segmentation and Iris Filter

In recent years, many Computer Aided Diagnosis (CAD) systems are suggested. Those systems can diagnose instead of a doctor, thus they are expected to reduce heavy burdens on the doctor during screening. The purpose of this study is to improve detection sensitivity for masses reducing the number of false positives as well as to extract mass regions accurately. In the proposed method, we focused on brightness and density of masses, thus we applied mean shift segmentation. After the segmentation, we obtained concentration of gradient vectors using Iris Filter and detected mass regions. According to the field test with a doctor, the proposed system was tested with 398 mammograms containing 193 masses. In the result of a performance test, a sensitivity of 81% was obtained at 5.0 false positives per image and 75% masses are detected at Area Overlap Measure (AOM) of more than 60%.