We have previously described how imaging systems and image reconstruction algorithms can be evaluated based on the ability of machine and human observers to perform a binary- discrimination task using the resulting images. Machine observers used in these investigations have been based on approximations to the ideal observer of Bayesian statistical decision theory. The present work is an evaluation of tomographic images reconstructed from a small number of views using the Cambridge Maximum Entropy software, MEMSYS 3. We compare the performance of machine and human viewers for the Rayleigh resolution task. Our results indicate that for both humans and machines a broad latitude exists in the choice of the parameter (alpha) that determines the smoothness of the reconstructions. We find human efficiency relative to the best machine observer to be approximately constant across the range of (alpha) values studied. The close correspondence between human and machine performance that we have now obtained over a variety of tasks indicate that our evaluation of imaging systems based on machine observers has relevance when the images are intended for human use.
Keywords: reconstruction comparison, reconstruction evaluation, reconstruction optimization, task performance, detectability index, figure of merit, machine observer, human observer, human efficiency, strength of prior, maximum-entropy reconstruction, MEMSYS 3
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