Medical Imaging: Image Perception, H. L. Kundel, ed., Proc. SPIE 2166, pp. 180-190 (1994)

Human and quasi-Bayesian obsevers of images limited by quantum noise, object variability, and artifacts

Kyle J. Myers, Robert F. Wagner, Kenneth M. Hanson, Harrison H. Barrett, and Jannik P. Rolland
Food and Drug Admin., Los Alamos National Lab., Univ. of Arizona


Many investigators have pointed out the need for performance measures that describe how well 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.

Keywords: task performance, statistical decision theory, human observers, Bayesian observers, quantum noise, artifacts, object variability

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