Name
Abstract | ||
|---|---|---|
Many rigid and affine registration methods rely on optimizing an intensity-based similarity criterion between images. Once registered, however, it is difficult to assess the quality of the registration based solely on the value of the similarity measure. Past work in quantitative error analysis relies on the availability of fiducial markers. Little work has been done on developing techniques that would permit assessing the registration quality between images that do not contain fiducial markers without manual intervention. In this paper, we present an automatic technique that permits to do so. We apply our method to estimate the registration quality of 10 MR and CT pairs and 10 MR and MR-contrast pairs. We show that our technique is capable of detecting cases with registration error larger than 2 degrees around one axis. We also show that our method is better able to identify error in MR to CT registrations than popular similarity measures. Work is under way to better determine the sensitivity of the technique. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1117/12.911556 | Proceedings of SPIE |
Keywords | Field | DocType |
Image registration,registration circuits,registration error,rigid registration | Affine transformation,Computer vision,Fiducial marker,Similarity measure,Computer science,Similarity criterion,Rigid body,Artificial intelligence,Image registration | Conference |
Volume | ISSN | Citations |
8314 | 0277-786X | 4 |
PageRank | References | Authors |
0.47 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan D. Datteri | 1 | 28 | 3.87 |
| Benoit M. Dawant | 2 | 1388 | 223.11 |