Name
Abstract | ||
|---|---|---|
In this article, we focus on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm. Remarkably, this logarithm is a simple 3D vector field, and is well-defined for diffeomorphisms close enough to the identity. This allows to perform vectorial statistics on diffeomorphisms, while preserving the invertibility constraint, contrary to Euclidean statistics on displacement fields. We also present here two efficient algorithms to compute logarithms of diffeomorphisms and exponentials of vector fields, whose accuracy is studied on synthetic data. Finally, we apply these tools to compute the mean of a set of diffeomorphisms, in the context of a registration experiment between an atlas an a database of 9 T1 MR images of the human brain. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11866565_113 | MICCAI |
Keywords | Field | DocType |
displacement field,principal logarithm,log-euclidean framework,invertibility constraint,invertible geometrical deformation,euclidean statistic,efficient algorithm,synthetic data,t1 mr image,vector field,human brain,lie groups,magnetic resonance imaging | Lie group,Euclidean vector,Exponential function,Vector field,Synthetic data,Logarithm,Euclidean geometry,Invertible matrix,Statistics,Mathematics | Conference |
Volume | Issue | ISSN |
9 | Pt 1 | 0302-9743 |
ISBN | Citations | PageRank |
3-540-44707-5 | 130 | 8.49 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincent Arsigny | 1 | 733 | 50.69 |
| Olivier Commowick | 2 | 505 | 39.81 |
| Xavier Pennec | 3 | 5021 | 357.08 |
| Nicholas Ayache | 4 | 10804 | 1654.36 |