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Proceedings Paper

Human vision model to predict observer performance: detection of microcalcifications as a function of monitor phosphor
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Paper Abstract

The goal was to develop an efficient method of optimizing CRT monitor performance for digital mammography. The Sarnoff JNDmetrix vision model is based on just-noticeable difference measurement and frequency-channel vision-modeling principles. Given 2 images as input the model returns accurate, robust estimates of discriminability. Model predictions are compared with human performance. Mammographic images with microcalcifications were viewed by six radiologists, once on a monitor with P45 and once on one with P104 phosphor. Results were compared with output of the model used to predict differences in perceptibility of calcifications using luminance data measured with a high-resolution CCD camera. Human performance was best with high contrast clusters and got worse with each decrease in contrast. Performance was better with the P45 than the P104 for targets at all contrast levels. The JNDmetrix model predicted the same pattern of results. Correlation between human and model observer performance was very high. We have demonstrated the utility of using a vision model to accurately predict human detection performance. The type of phosphor in a monitor influences observer performance at least for the detection of microcalcifications. The main reason is that the P104 has a higher luminance, but the P45 has a higher signal-to-noise ratio.

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: 10.1117/12.479979
Show Author Affiliations
Elizabeth A. Krupinski, Univ. of Arizona (United States)
Jeffrey P. Johnson, Sarnoff Corp. (United States)
Hans Roehrig, Univ. of Arizona (United States)
Jeffrey Lubin, Sarnoff Corp. (United States)
Michael Engstrom, Univ. of Arizona (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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