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8 May 1995 Risk assessment from automated feature analysis of digitized mammograms
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The identification of women at increased risk for breast cancer has important implications in both the surveillance for cancer and research into causes of the disease. The parenchymal pattern of the breast, revealed by mammography, and rated subjectively by observer, has been found to provide strong factors of risk for breast cancer. To provide a more quantitative measure of the proportion of mammographically dense tissue in the breast, we have previously described and evaluated an interactive technique in which an observer selects a threshold brightness level to separate dense from fatty tissue in the image. Measurement of mammographic density in this way provides an estimate of relative risk of 4, that is among leading indicators of the risk of developing breast cancer. To remove the variability associated with identification of thresholds by observer, we are investigating an automated threshold prediction based on independent features characterizing mammographic parenchyma. These features are based on regional measurements of image brightness variations (histogram analysis) and texture variations (fractal analysis) within digitized mammographic images. Preliminary results from an investigative model are presented.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey W. Byng, Martin Joel Yaffe, L. Little, G. Lockwood, Roberta A. Jong, E. Fishell, D. Tritchler, and Norman F. Boyd "Risk assessment from automated feature analysis of digitized mammograms", Proc. SPIE 2432, Medical Imaging 1995: Physics of Medical Imaging, (8 May 1995);

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