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Proceedings Paper

SVM-based visual-search model observers for PET tumor detection
Author(s): Howard C. Gifford; Anando Sen; Robert Azencott
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Paper Abstract

Many search-capable model observers follow task paradigms that specify clinically unrealistic prior knowledge about the anatomical backgrounds in study images. Visual-search (VS) observers, which implement distinct, feature-based candidate search and analysis stages, may provide a means of avoiding such paradigms. However, VS observers that conduct single-feature analysis have not been reliable in the absence of any background information. We investigated whether a VS observer based on multifeature analysis can overcome this background dependence. The testbed was a localization ROC (LROC) study with simulated whole-body PET images. Four target-dependent morphological features were defined in terms of 2D cross-correlations involving a known tumor profile and the test image. The feature values at the candidate locations in a set of training images were fed to a support-vector machine (SVM) to compute a linear discriminant that classified locations as tumor-present or tumor-absent. The LROC performance of this SVM-based VS observer was compared against the performances of human observers and a pair of existing model observers.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160X (20 March 2015); doi: 10.1117/12.2082942
Show Author Affiliations
Howard C. Gifford, Univ. of Houston (United States)
Anando Sen, Univ. of Houston (United States)
Robert Azencott, Univ. of Houston (United States)

Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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