Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most efficient technique in terms of identifying the suspicious lesions in the breast. However, the interpretation of this screening remains so hard and could lead to inaccurate detection known as false positive and false negative. Dense breast mammograms particularly are difficult to read because they may contain abnormal structures that are similar to the normal breast tissue. In this paper, we introduce an effective method for the detection of the ambiguous areas in digital mammograms. After noise and artifacts removal from the images using 2D Median filtering and labeling, we isolate the abnormalities using the meta-heuristic algorithm Electromagnetism-like Optimization (EML). The segmentation was carried on the abnormal cases of two different databases Mini-Mias and DDSM. The accuracy detection rate achieves almost 85% for both databases and 91.07% for DDSM alone.