Spatially adaptive log-euclidean polyaffine registration based on sparse matches

Authors:
Maxime Taquet;Benoît Macq;Simon K. Warfield
Affiliations:
ICTEAM Institute, Université Catholique de Louvain, Louvain-La-Neuve, Belgium and Computational Radiology Laboratory, Harvard Medical School;ICTEAM Institute, Université Catholique de Louvain, Louvain-La-Neuve, Belgium;Computational Radiology Laboratory, Harvard Medical School
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

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Abstract

Log-euclidean polyaffine transforms have recently been introduced to characterize the local affine behavior of the deformation in principal anatomical structures. The elegant mathematical framework makes them a powerful tool for image registration. However, their application is limited to large structures since they require the pre-definition of affine regions. This paper extends the polyaffine registration to adaptively fit a log-euclidean polyaffine transform that captures deformations at smaller scales. The approach is based on the sparse selection of matching points in the images and the formulation of the problem as an expectation maximization iterative closest point problem. The efficiency of the algorithm is shown through experiments on inter-subject registration of brain MRI between a healthy subject and patients with multiple sclerosis.