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12 March 2009 Scanning model observers to predict human performance in LROC studies of SPECT reconstruction using anatomical priors
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Abstract
We use scanning model observers to predict human performance in lesion search/detection study. The observer's task is to locate gallium-avid tumors in simulated SPECT images of a digital phantom. The goal of our model is to predict the optimal prior strength β for human observers of smoothing priors incorporated into the reconstruction algorithm. These priors use varying amounts of anatomical knowledge. We present results from a scanning channelized non-prewhitening matched filter, and compare them with results from a human-observer study. Including a step to mimic the greyscale perceptual-linearization used during the human-observer study improves the accuracy of the model. However we find that for lesions close to an organ boundary even the improved model does not accurately predict human performance.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andre Lehovich, Howard C. Gifford, and Michael A. King "Scanning model observers to predict human performance in LROC studies of SPECT reconstruction using anatomical priors", Proc. SPIE 7263, Medical Imaging 2009: Image Perception, Observer Performance, and Technology Assessment, 72631T (12 March 2009); https://doi.org/10.1117/12.813774
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