Coherence of Multiscale Features for Enhancement of Digital Mammograms

Abstract

Mammograms depict most of the significant changes in breast disease. The primary radiographic signs of cancer are related to tumor mass, density, size, borders, and shape, and local distribution of calcifications. We show that each of these features can be well described by coherence and orientation measures and provide visual cues for radiologists to identify possible lesions more easily without increasing false positives. In this paper, an artifact-free enhancement algorithm based on overcomplete multiscale representations is presented. First, an image was decomposed using a fast wavelet transform algorithm. At each level of analysis, energy and phase information are computed via a set of separable steerable filters. Then, a measure of coherence within each level was obtained by weighting an energy measure with the ratio of projections of local energy within a specified window. Each projection was computed onto the central point of a window with respect to the total energy within that window. Finally, a nonlinear operation, integrating coherence and orientation information, was applied to modify transform coefficients within distinct levels of analysis. These modified coefficients were then reconstructed, via an inverse fast wavelet transform, resulting in an improved visualization of significant mammographic features. The novelty of this algorithm lies in the detection of directional multiscale features and the removal of aliased perturbations