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Proceedings Paper

Human and quasi-Bayesian observers of images limited by quantum noise, object-variability, and artifacts
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Paper Abstract

Many investigators have pointed out the need for performance measures that describe how well the images produced by a medical imaging system aid the end user in performing a particular diagnostic task. To this end we have investigated a variety of imaging tasks to determine the applicability of Bayesian and related strategies for predicting human performance. We have compared Bayesian and human classification performance for tasks involving a number of sources of decision-variable spread, including quantum fluctuations contained in the data set, inherent biological variability within each patient class, and deterministic artifacts due to limited data sets.

Paper Details

Date Published: 1 April 1994
PDF: 11 pages
Proc. SPIE 2166, Medical Imaging 1994: Image Perception, (1 April 1994); doi: 10.1117/12.171740
Show Author Affiliations
Kyle J. Myers, Ctr. for Devices and Radiological Health/FDA (United States)
Robert F. Wagner, Ctr. for Devices and Radiological Health/FDA (United States)
Kenneth M. Hanson, Los Alamos National Lab. (United States)
Harrison H. Barrett, Arizona Health Sciences Ctr./Univ. of Arizona (United States)
Jannick P. Rolland, Univ. of North Carolina/Chapel Hill (United States)


Published in SPIE Proceedings Vol. 2166:
Medical Imaging 1994: Image Perception
Harold L. Kundel, Editor(s)

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