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Proceedings Paper

Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
Author(s): Marc Modat; Tom Vercauteren; Gerard R. Ridgway; David J. Hawkes; Nick C. Fox; Sébastien Ourselin
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Paper Abstract

The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios.

Paper Details

Date Published: 12 March 2010
PDF: 8 pages
Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76232K (12 March 2010); doi: 10.1117/12.843962
Show Author Affiliations
Marc Modat, Univ. College London (United Kingdom)
Tom Vercauteren, Mauna Kea Technologies (France)
Gerard R. Ridgway, Univ. College London (United Kingdom)
David J. Hawkes, Univ. College London (United Kingdom)
Nick C. Fox, Univ. College London (United Kingdom)
Sébastien Ourselin, Univ. College London (United Kingdom)

Published in SPIE Proceedings Vol. 7623:
Medical Imaging 2010: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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