Ideal and visual-search observers: accounting for anatomical noise in search tasks with planar nuclear imaging

Anando Sen; Howard C. Gifford

doi:10.1117/12.2081763

17 March 2015 Ideal and visual-search observers: accounting for anatomical noise in search tasks with planar nuclear imaging

Anando Sen, Howard C. Gifford

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Proceedings Volume 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment; 94160R (2015) https://doi.org/10.1117/12.2081763
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Model observers have frequently been used for hardware optimization of imaging systems. For model observers to reliably mimic human performance it is important to account for the sources of variations in the images. Detection-localization tasks are complicated by anatomical noise present in the images. Several scanning observers have been proposed for such tasks. The most popular of these, the channelized Hotelling observer (CHO) incorporates anatomical variations through covariance matrices. We propose the visual-search (VS) observer as an alternative to the CHO to account for anatomical noise. The VS observer is a two-step process which first identifies suspicious tumor candidates and then performs a detailed analysis on them. The identification of suspicious candidates (search) implicitly accounts for anatomical noise. In this study we present a comparison of these two observers with human observers. The application considered is collimator optimization for planar nuclear imaging. Both observers show similar trends in performance with the VS observer slightly closer to human performance.

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Anando Sen and Howard C. Gifford "Ideal and visual-search observers: accounting for anatomical noise in search tasks with planar nuclear imaging", Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160R (17 March 2015); https://doi.org/10.1117/12.2081763

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KEYWORDS

Collimators

Tumors

Performance modeling

Visualization

Matrices

Nuclear imaging

Image segmentation

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