Breast boarder boundaries extraction using statistical properties of Mammogram

Many image processing techniques developed over the past two decades to help radiologists in diagnosing breast cancer. At the same time, many studies proven that an early diagnosis of breast cancer can increase five year survival rate from 60% to 80+%. That made screening programs a mandatory step for females. Therefore, radiologists have to examine a large number of images, which may lead to missed breast lesions at early stage due to work load. Computer-Aided-Diagnosis (CAD) systems can be a powerful tool to overcome this problem by highlighting suspected lesions. However, this task is challenging also from CAD systems point of view due to difficulties in articulating and modeling patterns of abnormalities in a computational way as many pre-porcessing steps need to be done to identify region of interest before pattern recognition algorithms can be applied. In this paper a new proposed thresholding algorithm is introduced for breast boundaries and pectoral muscle determination in Mammograms using statistical properties.