<title>Automation in mammography: computer vision and human perception</title>

Abstract

Mammographic screening programs generate large numbers of highly variable, complex images, most of which are unequivocally normal. When present, abnormalities may be small or subtle. In this paper we focus on the detection and analysis of mammographic microcalcifications. We present results of experiments to determine factors affecting radiologists' perception of microcalcifications, and to investigate the effects of attention-cueing on detection performance. Our results show that radiologists' performance can be significantly improved with the use of prompts generated from automatically-detected microcalcification clusters. We also describe a new method for the identification and delineation of mammographic abnormalities for training and test purposes, based on the analysis of multiple high quality X-ray projections of excised lesions. Biopsy specimens are secured inside a rigid tetrahedron, the edges of which provide a reference frame to which the localizations of features can be related. Once a three-dimensional representation of an abnormality has been formed, it can be rotated to resemble the appearance in the original mammogram.