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22 May 2003 Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks
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Abstract
Previous work has shown that model observers can be used for automated optimization of human performance in clinically relevant detection tasks where the signal does not vary and is known to the observers (signal known exactly, SKE). In the present study, we investigate whether model observers can be used for automated optimization of a more clinically realistic task in which the signal varies in shape and size from trial to trial and is not known to the observer (signal known statistically, SKS). We specifically test the hypothesis of whether optimizing model observer in a computationally more tractable task in which the signal varies from trial to trial but is known to the observer (Signal known exactly but variable task, SKEV) leads to improved model and human performance in the SKS task. We optimized the JPEG 2000 encoder options to maximize performance of a particular model observer (non-prewhitening with an eye filter; NPWE) for a SKEV task using hybrid test images combining simulated signals and patient x-ray coronary angiograms. We then show that NPWE SKEV optimized JPEG 2000 encoder settings lead to an improved NPWE performance in the clinically more realistic SKS task. A follow up psychophysical study showed that human performance in the SKEV and SKS tasks improved by 18-24 % with the encoder options resulting from NPWE SKEV performance optimization. These findings suggest that model observer performance in the computationally more tractable SKEV task can be used to optimize human performance in the more clinically realistic SKS task using real anatomic backgrounds.
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Miguel P. Eckstein, Yani Zhang, Binh Pham, and Craig K. Abbey "Optimization of model observer performance for signal known exactly but variable tasks leads to optimized performance in signal known statistically tasks", Proc. SPIE 5034, Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment, (22 May 2003); https://doi.org/10.1117/12.480344
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