This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. Weshow that e cient representations may be identi ed from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: The hexagonal wavelet transform. Mammograms are reconstructed from transform coe cients modi ed at one or more levels by local and global non-linear operators. Multiscale edges identi ed within distinct levels of transform space provide local support for enhancement. In addition, we show that transform coe cients, modi ed (globally within each level) by an adaptive non-linear operator (histogram speci cation), can make moreobvious unseen or barely seen features of mammography without requiring additional radiation. In each case, multiscale edges and gain parameters are identi ed adaptively by the measure of energy within each level of scale-space. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be e ectively employed for the visualization of breast pathology without excessive noise ampli cation. By improving the visualization of breast pathology we can improve chances of early detection (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs). 1
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.