Conference Paper

Computer aided diagnosis system for classification of microcalcifications in digital mammograms

Osman M.E.
Wahed M.A.
Mohamed A.S.
Kadah Y.M.

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 x 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, 1st order statistics from wavelet coefficients algorithms; also, we extracted 1st 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.