Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer

Ali R. N. Avanaki; Kathryn Espig; Eddie Knippel; Tom R. L. Kimpe; Albert Xthona; Andrew D. A. Maidment

doi:10.1117/12.2217612

24 March 2016 Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer

Ali R. N. Avanaki, Kathryn Espig, Eddie Knippel, Tom R. L. Kimpe, Albert Xthona, Andrew D. A. Maidment

Author Affiliations +

Proceedings Volume 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment; 97870A (2016) https://doi.org/10.1117/12.2217612
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Ali R. N. Avanaki, Kathryn Espig, Eddie Knippel, Tom R. L. Kimpe, Albert Xthona, and Andrew D. A. Maidment "Location- and lesion-dependent estimation of background tissue complexity for anthropomorphic model observer", Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870A (24 March 2016); https://doi.org/10.1117/12.2217612

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