Abstract
Breast cancer is one of the most prevalent types of cancer that mainly affects the women population. chances of effective treatment increase with early diagnosis. Mammography is considered one of the effective and proven techniques for the early diagnosis of breast cancer. Tissues around masses look identical in a mammogram, which makes the automatic detection process a very challenging task; they are indistinguishable from the surrounding parenchyma. In this paper, we present an efficient and automated approach to segment masses in mammograms. The proposed method uses hierarchical clustering to isolate the salient area followed by extraction of features to reject false detection. We applied our method to two popular publicly available datasets (mini-MIAS and DDSM). A total of 56 images from the mini-mias database and 76 images from DDSM were randomly selected. Results are explained in terms of ROC (Receiver Operating Characteristics) curves and compared with other state-of-the-art techniques. Experimental results demonstrate the efficiency and advantages of the proposed system in automatic mass identification in mammograms.