Predicting detection task performance using a visual discrimination model

Dev Prasad Chakraborty

doi:10.1117/12.533270

4 May 2004 Predicting detection task performance using a visual discrimination model

Dev Prasad Chakraborty

Author Affiliations +

Proceedings Volume 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment; (2004) https://doi.org/10.1117/12.533270
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

In the visual discrimination model (VDM) approach to measuring image quality two input images are analyzed by an algorithm, which calculates the just-noticeable-index (JND) index, which is a measure of the perceptual difference between the two images in the discrimination task. It has been proposed that if one can simulate relevant lesions and backgrounds, the same method can be used to predict target detectability. One generates pairs of images which are exactly identical except for the presence of a lesion in one of them. The JND-index measured on this image pair is thought to correlate with target detectability, such as might be measure in a receiver operating characteristic (ROC) study, and some experimental studies supporting this idea have appeared. It is pointed out in this work that this method can lead to anomalous results, namely it does not predict the qualitative effect of lesion-size on lesion detectability in mammographic backgrounds. Another anomaly is that the method appears to work on single images, whereas the ROC method needs sets of normal and abnormal images. In this work we show that by modifying the method so that comparisons of near-identical images are avoided, it is possible to predict the lesion size dependence and avoid the clash with the ROC method.

Citation Download Citation

Dev Prasad Chakraborty "Predicting detection task performance using a visual discrimination model", Proc. SPIE 5372, Medical Imaging 2004: Image Perception, Observer Performance, and Technology Assessment, (4 May 2004); https://doi.org/10.1117/12.533270

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