Name
Title | ||
|---|---|---|
A probabilistic approach for the registration of images with missing correspondences. |
Abstract | ||
|---|---|---|
The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e. g. pathologies, most approaches encounter problems. The here proposed registration method is based on the use of probabilistic correspondences between sparse image representations, leading to a robust handling of potentially missing correspondences. A maximum-a-posteriori framework is used to derive the optimization criterion with respect to deformation parameters that aim to compensate not only spatial differences between the images but also appearance differences. A multi-resolution scheme speeds-up the optimization and increases the robustness. The approach is compared to a state-of-theart intensity-based variational registration method using MR brain images. The comprehensive quantitative evaluation using images with simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2511121 | Proceedings of SPIE |
Field | DocType | Volume |
Pattern recognition,Computer science,Artificial intelligence,Probabilistic logic | Conference | 10949 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julia Krüger | 1 | 16 | 7.63 |
| Jan Ehrhardt | 2 | 387 | 54.33 |
| Sandra Schultz | 3 | 4 | 2.11 |
| Heinz Handels | 4 | 1527 | 239.84 |