A Novel Approach to Detect Microcalcification in Mammogram Image using Evolutionary Algorithm

Breast cancer is one of the major causes for the increase in mortality among women, especially in developed countries. Brea cancer is the second most common cancer in women. The presence of microcalcifications in breast tissue is one of the most important signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this paper, detection of microcalcification is performed in two steps: preprocessing& enhancement, segmentation. First, the thresholding algorithm is applied for the breast boundary identification and a new proposed modified tracking algorithm is introduced for pectoral muscle determination in Mammograms. Second, the Genetic Algorithm (GA) and Artificial Bee Colony(ABC) is proposed to automatically detect the breast border and nipple position to identify the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is corresponding left and right images are subtracted to extract the suspicious region. The algorithms are tested on 161 patient’s digitized mammograms from MIAS database. In general theproposed GA, ABC and Bilateral algorithms are quite competitive with the other algorithms.