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Proceedings Paper

Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer
Author(s): Ali R. N. Avanaki; Kathryn Espig; Eddie Knippel; Tom R. L. Kimpe; Albert Xthona; Andrew D. A. Maidment
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Paper Abstract

In this paper, we specify a notion of background tissue complexity (BTC) as perceived by a human observer that is suited for use with model observers. This notion of BTC is a function of image location and lesion shape and size. We propose four unsupervised BTC estimators based on: (i) perceived pre- and post-lesion similarity of images, (ii) lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), (iii) tissue anomaly detection, and (iv) mammogram density measurement. The latter two are existing methods we adapt for location- and lesion-dependent BTC estimation. To validate the BTC estimators, we ask human observers to measure BTC as the visibility threshold amplitude of an inserted lesion at specified locations in a mammogram. Both human-measured and computationally estimated BTC varied with lesion shape (from circular to oval), size (from small circular to larger circular), and location (different points across a mammogram). BTCs measured by different human observers are correlated (ρ=0.67). BTC estimators are highly correlated to each other (0.84<rho;<0.95) and less so to human observers (ρ<=0.81). With change in lesion shape or size, estimated BTC by LBA changes in the same direction as human-measured BTC. A generalization of proposed methods for viewing breast tomosynthesis sequences in cine mode is outlined. The proposed estimators, as-is or customized to a specific human observer, may be used to construct a BTC-aware model observer, with applications such as optimization of contrast-enhanced medical imaging systems, and creation of a diversified image dataset with characteristics of a desired population.

Paper Details

Date Published: 24 March 2016
PDF: 13 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870A (24 March 2016); doi: 10.1117/12.2217612
Show Author Affiliations
Ali R. N. Avanaki, Barco, Inc. (United States)
Kathryn Espig, Barco, Inc. (United States)
Eddie Knippel, Barco, Inc. (United States)
Tom R. L. Kimpe, Barco N.V. (Belgium)
Albert Xthona, Barco, Inc. (United States)
Andrew D. A. Maidment, The Univ. of Pennsylvania (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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