Detection of breast cancer mass using MSER detector and features matching

Detection of breast cancer masses in mammogram images is an essential step in any computer-aided system for breast cancer diagnosis. In this paper, we propose a novel technique for breast cancer masses detection in mammograms based on the feature matching of different regions using Maximally Stable Extremal Regions (MSER). Firstly, a pre-processing step is applied to the original mammogram image to produce an enhanced version of this image. Then, MSER regions are extracted from both the original image and its enhanced version using MSER detector. Finally, feature matching process is applied between these regions to detect the mass area. The proposed algorithm has been tested on a collected set of 300 mammogram images containing abnormalities (i.e. benign and malignant masses) from four different databases. The proposed algorithm is able to accurately detect locations of masses with an accuracy of 95%. There aren’t any processing steps for pectoral muscle removal, this results in reducing the processing time. The average time taken by the proposed method to process one mammogram image is 0.14 s. The proposed method is fully automated and there is no need for user intervention or any readjustment. The proposed algorithm is robust against noise and it is not affected by the image quality, breast density category, or mass nature. The results show that the proposed algorithm has higher accuracy than the state of the art approaches.