We have previously described how imaging systems and image reconstruction algorithms can be evaluated on the basis of how well binary-discrimination tasks can be performed by a machine algorithm that `views' the reconstructions. Algorithms used in these investigations have been based on approximations to the ideal observer of Bayesian statistical decision theory. The present work examines the performance of an extended family of such algorithmic observers viewing tomographic images reconstructed from a small number of views using the Cambridge Maximum Entropy software, MEMSYS 3. We investigate the effects on the performance of these observers due to varying the parameter alpha ; this parameter controls the stopping point of the iterative reconstruction technique and effectively determines the smoothness of the reconstruction. For the detection task considered here, performance is maximum at the lowest values of alpha studied; these values are encountered as one moves toward the limit of maximum likelihood estimation while maintaining the positivity constraint intrinsic to entropic priors. A breakdown in the validity of a Gaussian approximation used by one of the machine algorithms (the posterior probability) was observed in this region. Measurements on human observers performing the same task show that they perform comparably to the best machine observers in the region of highest machine scores, i.e., smallest values of alpha . For increasing values of alpha , both human and machine observer performance degrade. The falloff in human performance is more rapid than that of the machine observer at the largest values of alpha (lowest performance) studied. This behavior is common to all such studies of the so-called psychometric function.
Keywords: reconstruction comparison, reconstruction evaluation, reconstruction optimization, task performance, detectability index, ideal observer, machine observer, human observer, human efficiency, psychometric function, strength of prior, maximum-entropy reconstruction, MEMSYS 3
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