Abstract
Breast cancer is the main cause of death for women between the ages of 35 to 55. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. Microcalcifications are among the earliest signs of a breast carcinoma. Actually, as radiologists point out, microcalcifications can be the only mammographic sign of non-palpable breast disease which are often overseen in the mammogram. In this paper a method is proposed to develop a Computer-Aided Diagnostic system for classification of microcalcifications in digital mammograms, it splits into three-step process. The first step is Region of Interest extraction of 32 times 32 pixels size. The second step is the features extraction, where we used a set of 234 features from Region of Interest by employing wavelet decomposition, 1 st order statistics from wavelet coefficients algorithms; also, we extracted 1 st order statistics, median contrast and local binary partition features. The third step is the classification process where differentiation between normal and abnormal is performed using a Minimum Distance Classifier and K-Nearest Neighbor Classifiers employing the leave-one-out training-testing methodology. The results show acceptable sensitivity and specificity for the proposed system.