Name
Abstract | ||
|---|---|---|
We present an extension of the diffeomorphic Geometric Demons algorithm which combines the iconic registration with geometric constraints. Our algorithm works in the log-domain space, so that one can efficiently compute the deformation field of the geometry. We represent the shape of objects of interest in the space of currents which is sensitive to both location and geometric structure of objects. Currents provides a distance between geometric structures that can be defined without specifying explicit point-to-point correspondences. We demonstrate this framework by registering simultaneously T1 images and 65 fiber bundles consistently extracted in 12 subjects and compare it against non-linear T1, tensor, and multi-modal T1 + Fractional Anisotropy (FA) registration algorithms. Results show the superiority of the Log-domain Geometric Demons over their purely iconic counterparts. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33418-4_8 | MICCAI (2) |
Keywords | Field | DocType |
iconic registration,algorithm work,model geometry,iconic counterpart,geometric structure,brain fiber log-demons registration,multi-modal t1,t1 image,geometric constraint,log-domain geometric demons,log-domain space,non-linear t1,diffeomorphism | Computer vision,Diffusion MRI,Fiber,Tensor,Computer science,Fractional anisotropy,Log domain,Artificial intelligence,Deformation (mechanics),Geometry,Diffeomorphism,Fiber bundle | Conference |
Volume | Issue | ISSN |
15 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
7 | 0.55 | 17 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Viviana Siless | 1 | 37 | 3.51 |
| Joan Glaunès | 2 | 262 | 13.63 |
| Pamela Guevara | 3 | 174 | 13.40 |
| Jean-François Mangin | 4 | 863 | 71.48 |
| C Poupon | 5 | 553 | 39.31 |
| D Le Bihan | 6 | 424 | 40.35 |
| Bertrand Thirion | 7 | 5047 | 270.40 |
| P Fillard | 8 | 1238 | 75.70 |