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A foveated channelized Hotelling search model predicts dissociations in human performance in 2D and 3D images
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Paper Abstract

We developed a foveated search model based on the channelized Hotelling observer (FCHO) that processes each image in parallel with Gabor channels which center frequencies decrease with retinal eccentricity (distance away from the fovea). The FCHO model is based on how radiologists read 3D volumes by scrolling through 2D slices. The model includes a search component that explores the image by making eye movements guided by peripheral processing and a slice scroll component that changes the current slice for the 3D images. We designed an experiment consisting of a free search in 2D and 3D noise filtered with power-law statistics matching mammograms. Two different targets were embedded in this background: one resembling a microcalcification and another one resembling a mass. Unlike traditional model observers (chanellized Hotelling and non-prewhitening matched filter with an eye filter), performance for the FCHO search model for both 2D and 3D search tasks showed similar dissociations as that of human observers. Detectability of a microcalcification-like target was significantly higher than for a mass-like target in 2D search but comparable or lower in 3D search. Together, our results suggest that 3D search tasks will require more computationally complex 3D search models that take into account the foveated properties of the visual system and eye movements to predict human observer performance in clinically relevant tasks.

Paper Details

Date Published: 4 March 2019
PDF: 8 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 109520D (4 March 2019); doi: 10.1117/12.2511777
Show Author Affiliations
Miguel A. Lago, Univ. of California, Santa Barbara (United States)
Craig K. Abbey, Univ. of California, Santa Barbara (United States)
Miguel P. Eckstein, Univ. of California, Santa Barbara (United States)


Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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