A lagrangian formulation for statistical fluid registration
Brun, C. C., Lepore, N., Pennec, X., Chou, Y. Y., Lee, A. D., Barysheva, M., de Zubicaray, G. I., McMahon, K. L., Wright, M. J., Toga, A. W., & Thompson, P. M. (2009) A lagrangian formulation for statistical fluid registration. In ISBI '09. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. Proceedings, IEEE, Boston, USA, pp. 975-978.
We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in , and used a Lagrangian framework to incorporate 0 th and 1st order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Keywords:||Genetics, Registration, Riemannian metrics, Statistical prior|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||Copyright 2009 IEEE|
|Deposited On:||09 Oct 2015 05:30|
|Last Modified:||21 Oct 2015 03:23|
Repository Staff Only: item control page