Segmentation of Microcalcifications in Mammograms: A comparative Study

Breast cancer is the most common type of cancer among women and it is the major cause of female cancer-related deaths worldwide. Microcalcifications, which are small crystals of calcium apatites, are considered the first sign of breast cancer in more than half of all breast cancer cases. Since these calcium apatites are very small and may be easily overlooked by the radiologists, automatic image processing systems can help radiologists in early diagnosis of breast cancer. In these systems, detection and segmentation of breast microcalcifications from the background tissue are important steps. The purpose of this work is to evaluate the performance of several breast microcalcifications segmentation techniques and select the best technique using a multicriteria decision making approach. The approaches were applied on 630 mammograms from the Digital Database for Screening Mammography. Of the 630 images, 315 images correspond to malignant cases and 315 correspond to benign cases. The efficiency of the considered techniques was evaluated by using five metrics, namely a similarity index, the extra overlap fraction, sensitivity, specificity, and accuracy.