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This paper develops and presents methods for the detection of features in high-resolution digital mammograms using anisotropic diffusion techniques. The automated or semiautomated analysis of digital mammograms for the purpose of detecting suspicious changes in normal tissue structure is an exceedingly important and elusive goal confronting researchers in digital mammography. The nature of the changes can be quite variable, but often the quality of the periphery of suspect lesions contains strong cues regarding the nature of the lesion. Thus, it is of interest to consider processing paradigms that analyze lesion boundary information, both to isolate suspect lesions from normal tissue and to aid in the differentiation of benign vs. malignant lesions. In this paper a modified version of the Malik-Perona nonlinear diffusion model is adopted that provides superior boundary detection capability while simultaneously strongly rejecting noise or irrelevant image artifacts. The algorithm provide a multiscale family of smoothed images that display the important property of intra-region smoothing without smoothing across boundaries. Thus, the features extracted do not suffer from the unnecessary blurring arising from conventional smoothing-differentiation edge detectors, while retaining the highly desirable property of noise elimination. In other words, the anisotropic diffusion method performs a piecewise smoothing of the mammographic data image. These properties make it possible to achieve high-quality segmentations of mammographic images. The output of the algorithm is a binary representation containing detailed structural information for the potentially interesting features in the mammogram. Thus, lesions containing spiculations or with associated microcalcifications can be represented with a high resolution, and subjected to further processing towards attaining the difficult goals of detection and diagnosis. The results of this technique as applied to digitized mammograms are presented, using mammogram X-rays digitized to 100 Micron spatial resolution and 12 bits of gray scale resolution.
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