Computerized Scheme for Focal Asymmetric Densities on Mammograms by Use of Geometric and Texture Analyses

Abstract

A focal asymmetric density (FAD), chiefly identified by comparing left breast and right breast mammograms, is a common finding related to breast cancers detected in mammograms. In our database, 18.4% of cases with any findings were identified as FADs. It is often difficult for radiologists to distinguish FADs from specific conditions of glandular tissue, and FADs are the second most frequently missed or misinterpreted reason for cases with interval cancers. Therefore, the purpose of this study was to develop a computerized method for detection of FADs in mammograms in order to assist earlier diagnoses of FADs by radiologists. Our computerized method for detection of FAD is based on geometric and texture analyses, and consisted of eight steps. Sixty-four images (32 FAD images and 32 normal images) were used for training and testing an artificial neural network (ANN) which distinguishes FADs from normal tissues. The performance of our computerized method was evaluated by a round-robin test. 84.4% of images with FAD (27/32) were correctly detected by the computerized method at 0.56 false positives per image. Our preliminary results show that the automated computerized scheme for detecting FAD on mammograms has the potential to assist radiologists in detecting FAD.