Paper
12 April 2002 Optimizing soft copy mammography displays using a human visual system model: influence of display phosphor
Author Affiliations +
Abstract
The goal of the project was to develop an efficient method of optimizing CRT performance for digital mammography. The paradigm measures radiologist performance for various display characteristics and uses these results to validate a model of human visual performance. The Sarnoff JNDmetrix vision model is based on psychophysical just-noticeable difference measurement and frequency-channel vision-modeling principles. Given 2 images as input the model returns accurate, robust estimates of their discriminability. Model predictions are compared with human performance. Mammographic images with microcalcifications were viewed by radiologists. Results were analyzed using ROC techniques. The images were viewed once on a monitor with P45 and once on a monitor with P104 phosphor. 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. Early results suggest that human performance is best with high contrast clusters and progressively gets worse with each decrease in contrast. Performance so far is better with the P45 than the P104 for targets at all contrast levels. The JNDmetrix model should predict the same pattern of results. The type of phosphor in a CRT monitor seems to influence observer performance.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Elizabeth A. Krupinski, Jeffrey P. Johnson, Hans Roehrig, and Jeffrey Lubin "Optimizing soft copy mammography displays using a human visual system model: influence of display phosphor", Proc. SPIE 4686, Medical Imaging 2002: Image Perception, Observer Performance, and Technology Assessment, (12 April 2002); https://doi.org/10.1117/12.462694
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Visual process modeling

Mammography

CRTs

Data modeling

Performance modeling

Visualization

Breast cancer

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