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Proceedings Paper

Predicting detection task performance using a visual discrimination model: II
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Paper Abstract

In the visual discrimination model (VDM) approach to measuring image quality two input images are analyzed by an algorithm that calculates a just-noticeable-difference (JND) index. It has been claimed that the JND-index can be used to predict target detectability in the medical imaging detection task and that it could "eventually replace the time-intensive and complicated ROC studies". In earlier work we have suggested that this claim may be incorrect. We showed that the JND-index and observer performance did not always correlate and that there were sometimes striking disagreements between the two. The purpose of this work is to present a modified method of using the VDM, termed "channelized-VDM" that correlates better with observer performance. A second purpose is to demonstrate another problem, namely predicting the optimal window-level setting of an image, where conventional VDM usage makes incorrect predictions. We show that the channelized VDM method makes better predictions in this case too. Based on our studies we caution against conventional VDM usage for image quality optimization in the medical imaging detection task. Additional material is available on the author's website (

Paper Details

Date Published: 6 April 2005
PDF: 8 pages
Proc. SPIE 5749, Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment, (6 April 2005); doi: 10.1117/12.594609
Show Author Affiliations
Dev Prasad Chakraborty, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 5749:
Medical Imaging 2005: Image Perception, Observer Performance, and Technology Assessment
Miguel P. Eckstein; Yulei Jiang, Editor(s)

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