Name
Title | ||
|---|---|---|
An efficient locally affine framework for the smooth registration of anatomical structures. |
Abstract | ||
|---|---|---|
Intra-subject and inter-subject nonlinear registration based on dense transformations requires the setting of many parameters, mainly for regularization. This task is a major issue, as the global quality of the registration will depend on it. Setting these parameters is, however, very hard, and they may have to be tuned for each patient when processing data acquired by different centers or using different protocols. Thus, we present in this article a method to introduce more coherence in the registration by using fewer degrees of freedom than with a dense registration. This is done by registering the images only on user-defined areas, using a set of affine transformations, which are optimized together in a very efficient manner. Our framework also ensures a smooth and coherent transformation thanks to a new regularization of the affine components. Finally, we ensure an invertible transformation thanks to the Log-Euclidean polyaffine framework. This allows us to get a more robust and very efficient registration method, while obtaining good results as explained below. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.media.2008.01.002 | Medical Image Analysis |
Keywords | Field | DocType |
Nonlinear registration,Locally affine transformation,Log-Euclidean regularization,Atlas-based brain segmentation | Brain segmentation,Affine transformation,Anatomy & histology,Computer vision,Medical imaging,Coherence (physics),Regularization (mathematics),Artificial intelligence,Invertible matrix,Mathematics,Computation | Journal |
Volume | Issue | ISSN |
12 | 4 | 1361-8415 |
Citations | PageRank | References |
33 | 1.91 | 27 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| O Commowick | 1 | 33 | 1.91 |
| Vincent Arsigny | 2 | 733 | 50.69 |
| A Isambert | 3 | 33 | 1.91 |
| J Costa | 4 | 33 | 1.91 |
| F Dhermain | 5 | 33 | 1.91 |
| F Bidault | 6 | 33 | 1.91 |
| P-Y Bondiau | 7 | 33 | 1.91 |
| Nicholas Ayache | 8 | 10804 | 1654.36 |
| G Malandain | 9 | 33 | 1.91 |