Human efficiency for detecting Gaussian signals in non-Gaussian distributed lumpy backgrounds using different display characteristics and scaling methods

Subok Park; Brandon Gallas; Aldo Badano; Nicholas Petrick; Kyle Myers

doi:10.1117/12.653902

17 March 2006 Human efficiency for detecting Gaussian signals in non-Gaussian distributed lumpy backgrounds using different display characteristics and scaling methods

Subok Park, Brandon Gallas, Aldo Badano, Nicholas Petrick, Kyle Myers

Author Affiliations +

Proceedings Volume 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment; 61460Q (2006) https://doi.org/10.1117/12.653902
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

A psychophysical study to measure human efficiency relative to the ideal observer for detecting a Gaussian signal at a known location in a non-Gaussian distributed lumpy background was conducted previously by the author. The study found that human efficiency was less than 2.2% while the ideal observer achieved 0.95 in terms of the area under the receiver operating characteristic curve. In this work, a psychophysical study was conducted with a number of changes that substantially improve upon the previous study design. First, a DICOM-calibrated monitor was used in this study for human-observer performance measurements, whereas an uncalibrated LCD monitor was used in the prior study. Second, two scaling methods to display image values were employed to see how scaling affects human performance for the same task. Third, a random order of image pairs was chosen for each observer to reduce any correlations in human performance that is likely caused by the fixed ordering of the image pairs in the prior study. Human efficiency relative to the ideal observer was found to be less than 3.8$% at the same performance level of the ideal observer as above. Our variance analysis indicates that neither scaling nor display made a significant difference in human performance on the tasks in the two studies. That is, we cannot say that either of these factors caused low human efficiency in these studies. Therefore, we conclude that using highly non-Gaussian distributed lumpy backgrounds in the tasks may have been the major source of low human efficiency.

Citation Download Citation

Subok Park, Brandon Gallas, Aldo Badano, Nicholas Petrick, and Kyle Myers "Human efficiency for detecting Gaussian signals in non-Gaussian distributed lumpy backgrounds using different display characteristics and scaling methods", Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460Q (17 March 2006); https://doi.org/10.1117/12.653902

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