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Proceedings Paper

Visual-search observers for SPECT simulations with clinical backgrounds
Author(s): Howard C. Gifford
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Paper Abstract

The purpose of this work was to test the ability of visual-search (VS) model observers to predict the lesion- detection performance of human observers with hybrid SPECT images. These images consist of clinical back- grounds with simulated abnormalities. The application of existing scanning model observers to hybrid images is complicated by the need for extensive statistical information, whereas VS models based on separate search and analysis processes may operate with reduced knowledge. A localization ROC (LROC) study involved the detection and localization of solitary pulmonary nodules in Tc-99m lung images. The study was aimed at op- timizing the number of iterations and the postfiltering of four rescaled block-iterative reconstruction strategies. These strategies implemented different combinations of attenuation correction, scatter correction, and detector resolution correction. For a VS observer in this study, the search and analysis processes were guided by a single set of base morphological features derived from knowledge of the lesion profile. One base set used difference-of- Gaussian channels while a second base set implemented spatial derivatives in combination with the Burgess eye filter. A feature-adaptive VS observer selected features of interest for a given image set on the basis of training-set performance. A comparison of the feature-adaptive observer results against previously acquired human-observer data is presented.

Paper Details

Date Published: 19 April 2016
PDF: 6 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870T (19 April 2016); doi: 10.1117/12.2217716
Show Author Affiliations
Howard C. Gifford, Univ. of Houston (United States)


Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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