An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Abstract

Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform and histogram specifications are used to enhance the visual representation of mammogram details. Then, local keypoints and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and confidence interval are very encouraging.