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Image Processing Algorithms for Digital Mammography: A Pictorial Essay¹

¹ From the Department of Radiology (E.D.P., B.M.H., S.R.A., R.E.J., M.E.B.), Lineberger Comprehensive Cancer Center (E.B.C.), and Department of Computer Science (S.M.P.), University of North Carolina, 101 Manning Dr, Chapel Hill, NC 27514-4226; the Department of Medical Imaging, University of Toronto, Ontario, Canada (M.J.Y.); the Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.D.A.M.); the Department of Radiology, University of Virginia, Charlottesville (M.B.W.); the Department of Radiology, University of Pennsylvania, Philadelphia (E.F.C.); the Department of Radiology, Johns Hopkins University, Baltimore, Md (L.L.F.); and the Department of Radiology, Massachusetts General Hospital, Boston (D.B.K.). Recipient of a Certificate of Merit award for a scientific exhibit at the 1998 RSNA scientific assembly. Received October 5, 1999; revision requested November 12; final revision received February 7, 2000; accepted February 16. Supported in part by grant RO1-CA60193-05 from the National Cancer Institute; grant 282-97-0078 from the Office of Women's Health, Department of Health and Human Services; grant DAMD 17-94-J-4345 from the U.S. Army Medical Research and Material Command; grant 7289 from the Canadian Breast Cancer Research Initiative; and grants RO1-CA6019, RO1-CA75145-01A1, and RO1-CA60183 from the National Cancer Institute. Address correspondence to E.D.P. (e-mail: etpisano@med.unc.edu).

Digital mammography systems allow manipulation of fine differences in image contrast by means of image processing algorithms. Different display algorithms have advantages and disadvantages for the specific tasks required in breast imaging—diagnosis and screening. Manual intensity windowing can produce digital mammograms very similar to standard screen-film mammograms but is limited by its operator dependence. Histogram-based intensity windowing improves the conspicuity of the lesion edge, but there is loss of detail outside the dense parts of the image. Mixture-model intensity windowing enhances the visibility of lesion borders against the fatty background, but the mixed parenchymal densities abutting the lesion may be lost. Contrast-limited adaptive histogram equalization can also provide subtle edge information but might degrade performance in the screening setting by enhancing the visibility of nuisance information. Unsharp masking enhances the sharpness of the borders of mass lesions, but this algorithm may make even an indistinct mass appear more circumscribed. Peripheral equalization displays lesion details well and preserves the peripheral information in the surrounding breast, but there may be flattening of image contrast in the nonperipheral portions of the image. Trex processing allows visualization of both lesion detail and breast edge information but reduces image contrast.

Index Terms: Breast radiography, 00.1215 • Images, display, 00.1215 • Images, processing, 00.1215 • Radiography, digital, 00.1215

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