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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Communication dans un congrès
Randy E. Ellis and Terry M. Peters. MICCAI - Medical Image Computing and Computer Assisted Interventions 2003, Oct 2003, Montreal, Canada. Springer, 2879, pp.804--811, 2003, Lecture Notes in Computer Science - LNCS. 〈10.1007/978-3-540-39903-2_98〉
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https://hal.inria.fr/hal-00875275
Contributeur : Project-Team Asclepios <>
Soumis le : lundi 21 octobre 2013 - 15:27:24
Dernière modification le : samedi 27 janvier 2018 - 01:30:49

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Radu Stefanescu, Xavier Pennec, Nicholas Ayache. Grid Enabled Non-Rigid Registration with a Dense Transformation and A Priori Information. Randy E. Ellis and Terry M. Peters. MICCAI - Medical Image Computing and Computer Assisted Interventions 2003, Oct 2003, Montreal, Canada. Springer, 2879, pp.804--811, 2003, Lecture Notes in Computer Science - LNCS. 〈10.1007/978-3-540-39903-2_98〉. 〈hal-00875275〉

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