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Proceedings Paper

MTF correction for optimizing softcopy display of digital mammograms: use of a vision model for predicting observer performance
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Paper Abstract

The goal of this project was to develop an efficient method of optimizing CRT monitor performance for digital mammography. In this study we examined the effects on performance of processing images to compensate for limitations in the MTF of the CRT monitor. The Sarnoff JNDmetrix vision model is based on just-noticeable difference measurement and frequency-channel vision-modeling principles. Given two images as input the model returns accurate, robust estimates of their discriminability. Model predictions are then compared with human performance. Mammographic images (n = 250) with microcalcifications were viewed by six radiologists. The images were viewed once in original unprocessed form and once after processing. Results were compared with output of the model that was used to predict differences in perceptibility of calcifications using luminance data measured with a high-resolution CCD camera. Human performance was better with the MTF compensated images at all contrast levels. The JNDmetrix model predicted the same pattern of results. Correlation between human and model observer performance was very high. Using image processing methods to compensate for limitations in the MTF of CRT monitors can improve the detection performance of radiologists searching for microcalcifications.

Paper Details

Date Published: 22 May 2003
PDF: 5 pages
Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); doi: 10.1117/12.479980
Show Author Affiliations
Elizabeth A. Krupinski, Univ. of Arizona (United States)
Hans Roehrig, Univ. of Arizona (United States)
Michael Engstrom, Univ. of Arizona (United States)
Jeffrey P. Johnson, Sarnoff Corp. (United States)
Jeffrey Lubin, Sarnoff Corp. (United States)

Published in SPIE Proceedings Vol. 5034:
Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment
Dev P. Chakraborty; Elizabeth A. Krupinski, Editor(s)

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