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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Stefanescu, R., Pennec, X., Ayache, N. (2003). Grid Enabled Non-rigid Registration with a Dense Transformation and a priori Information. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_98
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DOI: https://doi.org/10.1007/978-3-540-39903-2_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20464-0
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