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Grid Enabled Non-Rigid Registration with a Dense Transformation and A Priori Information

Radu Stefanescu 1 Xavier Pennec 1 Nicholas Ayache 1 
1 EPIDAURE - Medical imaging and robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Multi-subject non-rigid registration algorithms using dense transformations often encounter cases where the transformation to be estimated requires a large spatial variability. In these cases, linear regularization methods are not sufficient. In this paper, we present an algorithm that uses a priori information about the nature of the images in order to find more adapted deformations. We also present a robustness improvement that gives higher weight to those points in the images that contain more information. Finally, a fast parallel implementation using networked personal computers is presented. Results show that our method can take into account the large variability of the inner brain structures. A parallel implementation allowed us to execute the registration algorithm in 5 minutes and future improvements will open the possibility of registering massive quantities of images.
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Submitted on : Monday, October 21, 2013 - 3:27:24 PM
Last modification on : Friday, February 4, 2022 - 3:09:20 AM

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Radu Stefanescu, Xavier Pennec, Nicholas Ayache. Grid Enabled Non-Rigid Registration with a Dense Transformation and A Priori Information. MICCAI - Medical Image Computing and Computer Assisted Interventions 2003, Oct 2003, Montreal, Canada. pp.804--811, ⟨10.1007/978-3-540-39903-2_98⟩. ⟨hal-00875275⟩



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