Foveated model observers to predict human performance in 3D images

Miguel A. Lago; Craig K. Abbey; Miguel P. Eckstein

doi:10.1117/12.2252952

10 March 2017 Foveated model observers to predict human performance in 3D images

Miguel A. Lago, Craig K. Abbey, Miguel P. Eckstein

Proceedings Volume 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment; 101360P (2017) https://doi.org/10.1117/12.2252952
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

We evaluate 3D search requires model observers that take into account the peripheral human visual processing (foveated models) to predict human observer performance. We show that two different 3D tasks, free search and location-known detection, influence the relative human visual detectability of two signals of different sizes in synthetic backgrounds mimicking the noise found in 3D digital breast tomosynthesis. One of the signals resembled a microcalcification (a small and bright sphere), while the other one was designed to look like a mass (a larger Gaussian blob). We evaluated current standard models observers (Hotelling; Channelized Hotelling; non-prewhitening matched filter with eye filter, NPWE; and non-prewhitening matched filter model, NPW) and showed that they incorrectly predict the relative detectability of the two signals in 3D search. We propose a new model observer (3D Foveated Channelized Hotelling Observer) that incorporates the properties of the visual system over a large visual field (fovea and periphery). We show that the foveated model observer can accurately predict the rank order of detectability of the signals in 3D images for each task. Together, these results motivate the use of a new generation of foveated model observers for predicting image quality for search tasks in 3D imaging modalities such as digital breast tomosynthesis or computed tomography.

Conference Presentation

Citation Download Citation

Miguel A. Lago, Craig K. Abbey, and Miguel P. Eckstein "Foveated model observers to predict human performance in 3D images", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 101360P (10 March 2017); https://doi.org/10.1117/12.2252952

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