Authors : K. Geetha and K. Thanushkodi
Abstract: 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 study, the Genetic Algorithm (GA) hybrid with Particle Swarm Optimization (PSO) 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 proposed system consists of two steps: First, the mammogram images are enhanced using median filter, normalized the image, pectoral muscle region is removed and the border of the mammogram is detected for both left and right images from the binary image. Further PSO is applied to enhance the detected border. The figure of merit is calculated to evaluate whether the detected border is exact or not. And the nipple position is identified using ACS. The performance is compared with the existing methods. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. The algorithms are tested on 60 abnormal digitized mammograms from MIAS database.
K. Geetha and K. Thanushkodi , 2008. Particle Swarm Optimization for Automatic Detection of Breast Cancer . International Journal of Soft Computing, 3: 155-158.