Automatic identification of massive lesions in digitalized mammograms

Mammography is the most effective procedure for early diagnosis of the breast cancer. Computer-aided detection (CAD) system can be very helpful for radiologists in identification abnormalities earlier and faster than traditional screening program. In this paper, an automatic method to identify massive lesions in digitalized mammograms is proposed. The proposed method is a four-step method. In first step, image processing techniques is applied to enhance mammograms. This is followed by detection of the region-of-interest (ROI). Subsequently, Haralick-based features are extracted from the detected ROI. Finally, using artificial neural network, detected ROIs is classified as masses or non-masses based on extracted Haralick features. Our method is evaluated on Mini-MIAS database. The methods' performance is evaluated using Receiver Operating Characteristics (ROC) curve. The archived result A
z
=0.876 means that our method can be a quite effective tool in diagnosing breast cancer.