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14 September 1993 Characterization of the mammographic appearance of microcalcifications: applications in computer-aided diagnosis
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Abstract
This paper describes the application of the area and contrast of mammographic microcalcifications to computer-aided diagnostic schemes. Image contrast (measured in differences in optical density on the film) is converted to radiation contrast (in terms of log x- ray exposure) by correcting for the characteristic curve of the screen-film system and by correcting for the loss in contrast caused by the blurring by the screen and the film digitizer. From the radiation contrast, we estimate an effective thickness of a microcalcification that would have produced the corresponding radiation contrast. By examining the relationship between effective thickness and size of computer-detected signals (potential microcalcifications), the false-positive rate of our automated detection scheme can be reduced from 2.5 to 1.5 false clusters per image, while maintaining a sensitivity of 85%. We have also conducted two preliminary studies for which the extraction technique may be beneficial. The first was for classifying clusters as either benign or malignant. Four features were identified: the standard deviation in area, thickness, and effective volume of microcalcifications within a given cluster, and the mean effective volume of microcalcifications within the cluster. The second study was for developing a quantitative measure of the subtlety of appearance of microcalcifications in mammograms. We have found that the product of the area and image contrast summed over all microcalcifications within a cluster correlates well with human subjective impression of subtlety.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert M. Nishikawa, Yulei Jiang, Maryellen Lissak Giger, Carl J. Vyborny, Robert A. Schmidt, and Ulrich Bick "Characterization of the mammographic appearance of microcalcifications: applications in computer-aided diagnosis", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154528
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