Conference article

Accounting for Uncertainty in Medical Data: A CUDA Implementation of Normalized Convolution

S. Lindholm
Department of Science and Technology, Linköping University

J. Kronander
Department of Science and Technology, Linköping University

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Published in: Proceedings of SIGRAD 2011. Evaluations of Graphics and Visualization — Efficiency; Usefulness; Accessibility; Usability; November 17-18; 2011; KTH; Stockholm; Sweden

Linköping Electronic Conference Proceedings 65:6, p. 35-42

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Published: 2011-11-21

ISBN: 978-91-7393-008-6

ISSN: 1650-3686 (print), 1650-3740 (online)


The domain of medical imaging is naturally moving towards methods that can represent; and account for; local uncertainties in the image data. Even so; fast and efficient solutions that take uncertainty into account are not readily available even for common problems such as gradient estimation. In this work we present a CUDA implementation of Normalized Convolution; an uncertainty-aware image processing technique; well established in the signal processing domain. Our results show that up to 100X speedups are possible; which enables full resolution CT images to be processed at interactive processing speeds; fulfilling demands of both efficiency and interactivity that exist in the medical domain.


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