Name
Abstract | ||
|---|---|---|
Medical imaging applications of rigid and non-rigid elastic deformable image registration are undergoing wide scale development. Our approach determines image deformation maps through a hierarchical process, from global to local scales. Vemuri (2000) reported a registration method, based on levelset evolution theory, to morph an image along the motion gradient until it deforms to the reference image. We have applied this level set motion method as basis to iteratively compute the incremental motion fields and then we approximated the field using a higher-level affine and non-rigid motion model. In such a way, we combine sequentially the global affine motion, local affine motion and local non-rigid motion. Our method is fully automated, computationally efficient, and is able to detect large deformations if used together with multi-grid approaches, potentially yielding greater registration accuracy. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1117/12.710024 | PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) |
Keywords | Field | DocType |
non-rigid image registration,level set,optical flow,deformable image registration | Affine transformation,Computer vision,Motion field,Mathematical morphology,Medical imaging,Level set,Artificial intelligence,Motion estimation,Optical flow,Image registration,Mathematics | Conference |
Volume | ISSN | Citations |
6512 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deshan Yang | 1 | 50 | 14.88 |
| Joseph O. Deasy | 2 | 105 | 14.98 |
| Daniel A. Low | 3 | 39 | 3.98 |
| Issam El-Naqa | 4 | 528 | 36.31 |