Paper Info
Title
Contributions to 3D diffeomorphic atlas estimation: application to brain images.
Abstract
This paper focuses on the estimation of statistical atlases of 3D images by means of diffeomorphic transformations. Within a Log-Euclidean framework, the exponential and logarithm maps of diffeomorphisms need to be computed. In this framework, the Inverse Scaling and Squaring (ISS) method has been recently extended for the computation of the logarithm map, which is one of the most time demanding stages. In this work we propose to apply the Baker-Campbell-Hausdorff (BCH) formula instead. In a 3D simulation study, BCH formula and ISS method obtained similar accuracy but BCH formula was more than 100 times faster. This approach allowed us to estimate a 3D statistical brain atlas in a reasonable time, including the average and the modes of variation. Details for the computation of the modes of variation in the Sobolev tangent space of diffeomorphisms are also provided.
Year
DOI
Venue
2007
10.1007/978-3-540-75757-3_81
MICCAI
Keywords
Field
DocType
statistical atlas,diffeomorphic transformation,statistical brain atlas,bch formula,diffeomorphic atlas estimation,log-euclidean framework,brain image,sobolev tangent space,reasonable time,logarithm map,inverse scaling,iss method,3d imaging,brain imaging
Brain atlas,BCH code,Artificial intelligence,Logarithm,Baker–Campbell–Hausdorff formula,Computation,Computational anatomy,Exponential function,Pattern recognition,Algorithm,Mathematics,Calculus,Tangent space
Conference
Volume
Issue
ISSN
10
Pt 1
0302-9743
ISBN
Citations 
PageRank 
3-540-75756-2
26
1.64
References 
Authors
5
3
Name
Order
Citations
PageRank
Matias Bossa1302.43
Monica Hernandez217019.75
Salvador Olmos312713.87