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Proceedings Paper

Investigating the feasibility of using partial least squares as a method of extracting salient information for the evaluation of digital breast tomosynthesis
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Paper Abstract

Digital breast tomosynthesis (DBT) has shown promise for improving the detection of breast cancer, but it has not yet been fully optimized due to a large space of system parameters to explore. A task-based statistical approach1 is a rigorous method for evaluating and optimizing this promising imaging technique with the use of optimal observers such as the Hotelling observer (HO). However, the high data dimensionality found in DBT has been the bottleneck for the use of a task-based approach in DBT evaluation. To reduce data dimensionality while extracting salient information for performing a given task, efficient channels have to be used for the HO. In the past few years, 2D Laguerre-Gauss (LG) channels, which are a complete basis for stationary backgrounds and rotationally symmetric signals, have been utilized for DBT evaluation2, 3 . But since background and signal statistics from DBT data are neither stationary nor rotationally symmetric, LG channels may not be efficient in providing reliable performance trends as a function of system parameters. Recently, partial least squares (PLS) has been shown to generate efficient channels for the Hotelling observer in detection tasks involving random backgrounds and signals.4 In this study, we investigate the use of PLS as a method for extracting salient information from DBT in order to better evaluate such systems.

Paper Details

Date Published: 28 March 2013
PDF: 11 pages
Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 867311 (28 March 2013); doi: 10.1117/12.2008018
Show Author Affiliations
George Z. Zhang, U.S. Food and Drug Administration (United States)
Kyle J. Myers, U.S. Food and Drug Administration (United States)
Subok Park, U.S. Food and Drug Administration (United States)

Published in SPIE Proceedings Vol. 8673:
Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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