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Journal of Medical Imaging

Location- and lesion-dependent estimation of mammographic background tissue complexity
Author(s): Ali Avanaki; Kathryn Espig; Tom Kimpe
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Paper Abstract

We specify a notion of perceived background tissue complexity (BTC) that varies with lesion shape, lesion size, and lesion location in the image. We propose four unsupervised BTC estimators based on: perceived pre and postlesion similarity of images, lesion border analysis (LBA; conspicuous lesion should be brighter than its surround), tissue anomaly detection, and local energy. The latter two are existing methods adapted for location- and lesion-dependent BTC estimation. For evaluation, we ask human observers to measure BTC (threshold visibility amplitude of a given lesion inserted) at specified locations in a mammogram. As expected, both human measured and computationally estimated BTC vary with lesion shape, size, and location. BTCs measured by different human observers are correlated ( ρ = 0.67 ). BTC estimators are correlated to each other ( 0.84 < ρ < 0.95 ) and less so to human observers ( ρ 0.81 ). With change in lesion shape or size, LBA estimated BTC changes in the same direction as human measured BTC. Proposed estimators can be generalized to other modalities (e.g., breast tomosynthesis) and used as-is or customized to a specific human observer, to construct BTC-aware model observers 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: 12 January 2017
PDF: 10 pages
J. Med. Img. 4(1) 015501 doi: 10.1117/1.JMI.4.1.015501
Published in: Journal of Medical Imaging Volume 4, Issue 1
Show Author Affiliations
Ali Avanaki, Barco, Inc. (United States)
Kathryn Espig, Barco, Inc. (United States)
Tom Kimpe, Barco N.V. (Belgium)


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