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Proceedings Paper

Comparison of texture synthesis methods for content generation in ultrasound simulation for training
Author(s): Oliver Mattausch; Elizabeth Ren; Michael Bajka; Kenneth Vanhoey; Orcun Goksel
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Paper Abstract

Navigation and interpretation of ultrasound (US) images require substantial expertise, the training of which can be aided by virtual-reality simulators. However, a major challenge in creating plausible simulated US images is the generation of realistic ultrasound speckle. Since typical ultrasound speckle exhibits many properties of Markov Random Fields, it is conceivable to use texture synthesis for generating plausible US appearance. In this work, we investigate popular classes of texture synthesis methods for generating realistic US content. In a user study, we evaluate their performance for reproducing homogeneous tissue regions in B-mode US images from small image samples of similar tissue and report the best-performing synthesis methods. We further show that regression trees can be used on speckle texture features to learn a predictor for US realism.

Paper Details

Date Published: 3 March 2017
PDF: 10 pages
Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 1013523 (3 March 2017); doi: 10.1117/12.2250604
Show Author Affiliations
Oliver Mattausch, Computer Vision Lab., ETH Zürich (Switzerland)
Elizabeth Ren, Computer Vision Lab., ETH Zürich (Switzerland)
Michael Bajka, Univ. Hospital Zurich (Switzerland)
Kenneth Vanhoey, Computer Vision Lab., ETH Zürich (Switzerland)
Orcun Goksel, Computer Vision Lab., ETH Zürich (Switzerland)


Published in SPIE Proceedings Vol. 10135:
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Baowei Fei, Editor(s)

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