Detection of tumor in mammographic images by hierarchy of block's features

In the world, breast cancer in female population has increased significantly in recent years. In this paper, we present a new method for circumscribed masses detection in mammograms. First of all, the original mammogram is preprocessed to remove unwanted regions such as label, pectoral muscle and small bright spots similar to mass. Next, we divide the mammogram into equal blocks and calculate statistical characteristics or features for each block. Then, each block is classified as abnormal or normal block based on hierarchy of its features. Adjacent abnormal blocks are then merged into suspicious region. A sensitivity of 95.3% with only 0.48 false alarms per image is observed when evaluating the proposed method on mammographic images from Mini-MIAS database.