Over the past 15 years, considerable progress has been made in the development of computer-aided detection (CAD) of abnormalities in mammograms. Nevertheless, because of performance limitations of current CAD algorithms, the question of whether CAD provides a net benefit remains unresolved. In recent years, despite considerable effort by many groups, the rate of improvement in CAD performance has declined to the point that performance statistics seem to be approaching an asymptote, which is well below the performance of mammographers. The most likely reason for this is that essentially all current CAD implementations are founded on traditional methods of signal processing and pattern recognition and derive their performance by detecting features in a single image. These features are then classified by some inference mechanism. It is conceivable (probable) that most of the relevant physical features in single images have been identified and exploited to some extent. The hypothesis of this proposal is that performance limitations of current CAD, as indicated by the difference in performance between CAD and mammographers, result to a large extent from the failure of these algorithms to utilize data that can only be derived by a synergistic analysis of multiple images. Thus, it is the intent of this proposal to extend current CAD methodology to enable the extraction of information related to the spatial structure of a breast from ipsilateral views. Our preliminary results have established that despite compression-induced distortion, there are features that can be derived automatically from pairs of images and have been shown to provide information not obtainable from the independent analysis of single images. These multi-image-based features are partially independent of tissue distortion from breast compression during mammography. We will investigate and refine these and other features that can be identified. The purpose of this investigation is to fully exploit these kinds of features and optimize their contributions to a multi-image-based CAD algorithm.
