Spatially Adaptive Log-Euclidean Polyaffine Registration based on Sparse Matches
(2011) Medical Image Computing and Computer Assisted Interventions — Location: Toronto, Canada (18.September.2011)
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- Abstract
- Log-euclideanpolyaffinetransformshaverecentlybeenintro- duced to characterize the local affine behavior of the deformation in prin- cipal 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 max- imization 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.
- Affiliations
Harvard Medical School Computational Radiology Laboratory
Citations
Taquet, M., Macq, B., & Warfield, S. K. (2011). Spatially Adaptive Log-Euclidean Polyaffine Registration based on Sparse Matches. In Gabor Fichtinger, Anne Martel and Terry Peters (ed.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011 (pp. 590-597). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-23629-7_72