Name
Abstract | ||
|---|---|---|
Probabilistic image registration methods estimate the posterior distribution of transformation. The conventional way of interpreting the transformation posterior is to use the mode as the most likely transformation and assign its corresponding intensity to the registered voxel. Meanwhile, summary statistics of the posterior are employed to evaluate the registration uncertainty, that is the trustworthiness of the registered image. Despite the wide acceptance, this convention has never been justified. In this paper, based on illustrative examples, we question the correctness and usefulness of conventional methods. In order to faithfully translate the transformation posterior, we propose to encode the variability of values into a novel data type called ensemble fields. Ensemble fields can serve as a complement to the registered image and a foundation for developing advanced methods to characterize the uncertainty in registration-based tasks. We demonstrate the potential of ensemble fields by pilot examples. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Voxel,Data mining,Computer science,Correctness,Posterior probability,Data type,Artificial intelligence,Probabilistic logic,ENCODE,Pattern recognition,Mode (statistics),Machine learning,Image registration |
DocType | Volume | Citations |
Journal | abs/1604.01889 | 0 |
PageRank | References | Authors |
0.34 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luo Jie | 1 | 37 | 8.83 |
| Karteek Popuri | 2 | 59 | 8.80 |
| Dana Cobzas | 3 | 207 | 22.19 |
| Hongyi Ding | 4 | 0 | 1.01 |
| Masashi Sugiyama | 5 | 3353 | 264.24 |