Computer aided diagnosis of digital mammograms

The high incidence of breast cancer in women has increased significantly in the recent years. Mammogram -breast x-ray imaging -is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15 to 30% of masses referred for surgical biopsy are actually malignant. Physician experience of detecting breast cancer can be assisted by using some computerized feature extraction algorithms. We are introducing, as an aid to radiologists, a computer diagnosis system, which could be helpful in diagnosing abnormalities faster than traditional screening program without the drawback attribute to human factors. The techniques used in this paper for feature extraction is based on the invariant features and fractal dimensions of locally processed image (ROI). Two statistical classifiers (The minimum distance classifier and the voting K-Nearest Neighbor classifier) were used and compared through the system to reach a better classification decision.