Automated Edge Detection of Breast Masses on Mammograms

Edge of a breast mass is one of the indicators of breast abnormality detection. In a mammogram, round and circumscribed masses indicate benign changes and malignant masses usually has speculated (irregular) boundary. The paper has encountered a fundamental problem of active contour model which was first proposed by Kass et al. The problem encountered here is generation of initial contour points manually selected by users. Thus the positions of initial contour points will vary with human perspective, which is very difficult to identify actual and accurate contour points. To overcome this problem to some extent, sobel edge detection method is used as a prior step of active contour model. Experiments have been tested on a dataset of 160 mammograms collected from Mini-M1AS benchmark database and compared with sobel edge detection method. In experiments, 92.5% segmentation accuracy has been obtained with sensitivity 93% and 85% specificity where the sobel edge detection method shown very less segmentation accuracy of 84% with 91% sensitivity and 50% specificity. Time complexity and detection error have been also analysed for proposed method, ideal high pass filter, sobel edge detection, bough transform and active contour model.