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Simpson, I.J.A.* ; Schnabel, J.A.* ; Groves, A.R.* ; Andersson, J.L.R.* ; Woolrich, M.W.*

Probabilistic inference of regularisation in non-rigid registration.

Neuroimage 59, 2438-2451 (2012)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
A long-standing issue in non-rigid image registration is the choice of the level of regularisation. Regularisation is necessary to preserve the smoothness of the registration and penalise against unnecessary complexity. The vast majority of existing registration methods use a fixed level of regularisation, which is typically hand-tuned by a user to provide "nice" results. However, the optimal level of regularisation will depend on the data which is being processed; lower signal-to-noise ratios require higher regularisation to avoid registering image noise as well as features, and different pairs of images require registrations of varying complexity depending on their anatomical similarity. In this paper we present a probabilistic registration framework that infers the level of regularisation from the data. An additional benefit of this proposed probabilistic framework is that estimates of the registration uncertainty are obtained. This framework has been implemented using a free-form deformation transformation model, although it would be generically applicable to a range of transformation models. We demonstrate our registration framework on the application of inter-subject brain registration of healthy control subjects from the NIREP database. In our results we show that our framework appropriately adapts the level of regularisation in the presence of noise, and that inferring regularisation on an individual basis leads to a reduction in model over-fitting as measured by image folding while providing a similar level of overlap. © 2011 Elsevier Inc.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Bayesian Modelling ; Registration ; Regularisation
ISSN (print) / ISBN 1053-8119
e-ISSN 1095-9572
Journal NeuroImage - a Journal of Brain Function
Quellenangaben Volume: 59, Issue: 3, Pages: 2438-2451 Article Number: , Supplement: ,
Publisher Elsevier
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)