The disagreement between the ideal observer and human observers in hardware and software imaging system optimization: theoretical explanations and evidence

Xin He

doi:10.1117/12.2252169

10 March 2017 The disagreement between the ideal observer and human observers in hardware and software imaging system optimization: theoretical explanations and evidence

Xin He

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Proceedings Volume 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment; 1013610 (2017) https://doi.org/10.1117/12.2252169
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

The ideal observer is widely used in imaging system optimization. One practical question remains open: do the ideal and human observers have the same preference in system optimization and evaluation? Based on the ideal observer’s mathematical properties proposed by Barrett et. al. and the empirical properties of human observers investigated by Myers et. al., I attempt to pursue the general rules regarding the applicability of the ideal observer in system optimization. Particularly, in software optimization, the ideal observer pursues data conservation while humans pursue data presentation or perception. In hardware optimization, the ideal observer pursues a system with the maximum total information, while humans pursue a system with the maximum selected (e.g., certain frequency bands) information. These different objectives may result in different system optimizations between human and the ideal observers. Thus, an ideal observer optimized system is not necessarily optimal for humans. I cite empirical evidence in search and detection tasks, in hardware and software evaluation, in X-ray CT, pinhole imaging, as well as emission computed tomography to corroborate the claims. (Disclaimer: the views expressed in this work do not necessarily represent those of the FDA)

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Xin He "The disagreement between the ideal observer and human observers in hardware and software imaging system optimization: theoretical explanations and evidence", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 1013610 (10 March 2017); https://doi.org/10.1117/12.2252169

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KEYWORDS

Imaging systems

Image processing

Image restoration

Linear filtering

Medical imaging

Reconstruction algorithms

Signal detection

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