Paper Info
Title
A Lagrangian formulation for statistical fluid registration
Abstract
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 algorithm developed in [4], and used a Lagrangian framework to incorporate 0th 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 tensor-based distances to compare the power and the robustness of the algorithms.
Year
DOI
Venue
2009
10.1109/ISBI.2009.5193217
ISBI
Keywords
Field
DocType
Riemannian metrics,genetics,registration,statistical prior
Lagrangian mechanics,Population,Computer vision,Vector field,Computer science,Regularization (mathematics),Artificial intelligence,Covariance matrix,Order statistic,Image registration,Covariance
Conference
Volume
Citations 
PageRank 
2009
2
0.48
References 
Authors
3
11
Name
Order
Citations
PageRank
Caroline C. Brun1936.42
Natasha Lepore213415.80
Xavier Pennec35021357.08
Yi-Yu Chou429022.25
Agatha D Lee529623.02
Marina Barysheva626922.02
Greig I. De Zubicaray749143.04
Katie L. Mcmahon837433.49
Margaret J. Wright943639.31
Arthur W. Toga103128261.46
Paul Thompson113860321.32