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16 April 1996 Reproducibility of an automated scheme for the detection of clustered microcalcifications on digital mammograms
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Abstract
Computer-aided diagnosis can be defined as a diagnosis made by a radiologist who takes into account the computerized analysis of the radiograph. The potential advantage of this approach is that errors by human observers that are caused by intra-observer variation, can be caught by the computer, which performs a thorough, methodical search of the image. However, if the input image to computer comes from a digitized radiograph, then the process of digitization introduces variability into the computer analysis of the image. Because the film digitizer samples the image, re-digitization will not necessarily produce the exact same digital image, and subsequently, the computerized scheme may produce different results on the different samples of the original image. The purpose of this study was to examine the amount of variability introduced by film digitization by measuring the reproducibility of our computerized scheme for the detection of clustered microcalcifications on mammograms when the mammogram is digitized a number of times. We have found in general that if the detected cluster (either a true or false cluster) corresponds to some obvious anatomical structure, then the cluster will be re-detected upon re-digitization. If, however, the cluster corresponds to low contrast objects within the breast, including image noise, then the cluster is not likely to be re- detected. As a result, true clusters tend to be much more reproducible than false-positive clusters. In the future, when direct digital radiographs are used as input to the computerized schemes, problems with reproducibility will not exist.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert M. Nishikawa, John Papaioannou, and Stephen A. Collins "Reproducibility of an automated scheme for the detection of clustered microcalcifications on digital mammograms", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237942
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