Clusters of microcalcifications in mammograms are an important early sign of breast cancer in women. In this paper an approach is proposed to develop a Computer-Aided Diagnosis (CAD) system that can be very helpful for radiologist in diagnosing microcalcifications' patterns in digitized mammograms earlier and faster than typical screening programs. The proposed method has been implemented in three stages: (a) the region of interest (ROI) selection of 32×32 pixels size which identifies clusters of microcalcifications, (b) the feature extraction stage is based on the wavelet decomposition of locally processed image (region of interest) to compute the important features of each cluster and (c) the classification stage, which classify between normal and microcalcifications' patterns and then classify between benign and malignant microcalcifications. In classification stage, four methods were used, the voting K-Nearest Neighbor classifier (K-NN), Support Vector Machine (SVM) classifier, Neural Network (NN) classifier, and Fuzzy classifier. The proposed method was evaluated using the Mammographic Image Analysis Society (MIAS) mammographic databases. The proposed system was shown to have the large potential for microcalcifications detection in digital mammograms.

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Mammogram-breast x-ray 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. Computer-Aided Classification system was used to help in diagnosing abnormalities faster than traditional screening program without the drawback attribute to human factors. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256×256 pixels size. The second step is the feature extraction; we used a set of 88 features and we found that 78 of these feature are capable of differentiating between normal and cancerous breast tissues in order to minimize the classification error. The third step is the classification process; we used the technique of the k-Nearest Neighbor (k-NN) to classify between normal and cancerous tissues. The proposed system was shown to have the large potential for cancer detection from digital mammograms.

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A new methodology for computer aided diagnosis in digital mammography using unsupervised classification and classdependent feature selection is presented. This technique considers unlabeled data and provides unsupervised classes that give a better insight into classes and their interrelationships, thus improving the overall effectiveness of the diagnosis. This technique is also extended to utilize biclustering methods, which allow for definition of unsupervised clusters of both pathologies and features. This has potential to provide more flexibility, and hence better diagnostic accuracy, than the commonly used feature selection strategies. The developed methods are applied to diagnose digital mammographic images from the Mammographic Image Analysis Society (MIAS) database and the results confirm the potential for improving the current diagnostic rates.