Double reading can improve the detection rate, but it is too expensive and time consuming. It is reported that about 10–25 % abnormal cases shown in mammography have been wrongly ignored by radiologists. However, due to the complexity of the breast structure, low disease prevalence (approximately 0.5 % ), and radiologist fatigue, abnormalities are often ignored. The screening programs generate large volumes of mammograms to be analyzed. Among all the diagnostic methods currently available for detection of breast cancer, mammography is regarded as the only reliable and practical method capable of detecting breast cancer in its early stage. In order to detect it in its early stage, many countries have established screening programs. Studies have indicated that early detection and treatment improve the survival chances of the patients. The World Health Organization estimated that 521,907 women worldwide died in 2012 due to breast cancer. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.īreast cancer is the most frequent form of cancer in women and is also the leading cause of mortality in women each year. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. Finally, the MC regions are analyzed with spatial information to locate MC clusters. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. For each image, suspicious MC regions are extracted with region growing and active contour segmentation. In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). Their accurate detection is important in computer-aided detection (CADe). Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women.
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