This paper introduces a novel approach for accomplishing mammograp/zic feature analysis through overcomplete inultiresolution representations. We show that efficient representations may be identifiedfrom digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space andfrequency, results in a powetful methodologyfor image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations : (1) Dyadic wavelet transform (2) q-transform. Mammograms are reconstructedfrom transform coefficients moc4fled at one or more levels by nonlinear, logarithmic and constant scale-space weightfunctions. Multiscale edges identified within distinct levels of transform space provide a local supportfor enhancement throughout each decomposition. In addition, transform coefficients are modified by histogram specification within distinct level of transform space. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification. 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 (low costs).
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