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According to Breast Cancer Institute (BCI), Breast cancer is one of the most dangerous types of cancer that affects women all around the world. Based on clinical guidelines, the use of mammogram for an early detection of this cancer is an important step in reducing its danger. Thus, computer aided detection using image processing techniques in analyzing mammogram images and localizing abnormalities such as mass has been used. A False Positive (FP) rate is considered a challenge in localizing mass in mammogram images. Hence, in this paper, the rejection model based on the Support Vector Machine (SVM) has been used in reducing the FP rate of segmented mammogram images using the Chan-Vese method, initialized by the Marker Controller Watershed (MCWS) algorithm. Firstly, a mammogram image is segmented using the MCWS algorithm. Then, the segmentation is refined using Chan-Vese. After …