Visual-search models for location-known detection tasks

H. C. Gifford; Z. Karbaschi; K. Banerjee; M. Das

doi:10.1117/12.2254456

10 March 2017 Visual-search models for location-known detection tasks

H. C. Gifford, Z. Karbaschi, K. Banerjee, M. Das

Proceedings Volume 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment; 1013612 (2017) https://doi.org/10.1117/12.2254456
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Lesion-detection studies that analyze a fixed target position are generally considered predictive of studies involving lesion search, but the extent of the correlation often goes untested. The purpose of this work was to develop a visual-search (VS) model observer for location-known tasks that, coupled with previous work on localization tasks, would allow efficient same-observer assessments of how search and other task variations can alter study outcomes. The model observer featured adjustable parameters to control the search radius around the fixed lesion location and the minimum separation between suspicious locations. Comparisons were made against human observers, a channelized Hotelling observer and a nonprewhitening observer with eye filter in a two-alternative forced-choice study with simulated lumpy background images containing stationary anatomical and quantum noise. These images modeled single-pinhole nuclear medicine scans with different pinhole sizes. When the VS observer’s search radius was optimized with training images, close agreement was obtained with human-observer results. Some performance differences between the humans could be explained by varying the model observer’s separation parameter. The range of optimal pinhole sizes identified by the VS observer was in agreement with the range determined with the channelized Hotelling observer.

Citation Download Citation

H. C. Gifford, Z. Karbaschi, K. Banerjee, and M. Das "Visual-search models for location-known detection tasks", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 1013612 (10 March 2017); https://doi.org/10.1117/12.2254456

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