Wavelet methods for combining CAD with enhancement of mammograms

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Abstract
Computer-aided diagnosis techniques have been proposed as second opinion providers in digital mammography. This paper considers a new method of presenting CAD output to the radiologist. Instead of superimposing detected pixels or arrows on the mammogram, we adaptively enhance the most suspicious regions according to the weight indicated by the test statistic at the detector output. In so doing, CAD false positives promise to be less obtrusive to the viewer, and lesions missed by CAD (false negatives) may still be detected by the radiologist. In our method the entire mammogram is enhanced to some (spatially varying) degree. Enhancement is realized by applying nonlinear operators to wavelet coefficients computed at multiple scales. We combine this technique with the results of our previous wavelets-based CAD algorithm for detecting microcalcifications.

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