Modat, M.;
Vercauteren, T.;
Ridgway, G.R.;
Hawkes, D.J.;
Fox, N.C.;
Ourselin, S.;
(2010)
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images.
In: Dawant, B.M. and Haynor, D.R., (eds.)
Medical Imaging 2010: Image Processing.
(pp. 76232K).
International Society for Optical Engineering: Bellingham, US.
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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.
Type: | Proceedings paper |
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Title: | Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1117/12.843962 |
Publisher version: | http://dx.doi.org/10.1117/12.843962 |
Language: | English |
Additional information: | Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/19174 |
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