Name
Abstract | ||
|---|---|---|
•Presenting a novel deformable registration paradigm using statistical shape models.•Developed three algorithms that use different features and noise model assumptions.•Experiments with simulated data show submillimeter registrations, reconstructions.•Preliminary results on in-vivo clinical data also show promising results. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.media.2019.04.013 | Medical Image Analysis |
Keywords | Field | DocType |
Deformable most-likely-point paradigm,Statistical shape models,Deformable registration,Shape inference | Computer vision,Pattern recognition,Artificial intelligence,Generative grammar,Mathematics | Journal |
Volume | ISSN | Citations |
55 | 1361-8415 | 3 |
PageRank | References | Authors |
0.40 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayushi Sinha | 1 | 24 | 6.72 |
| Seth Billings | 2 | 35 | 4.93 |
| Austin Reiter | 3 | 164 | 13.02 |
| Xingtong Liu | 4 | 13 | 5.02 |
| Masaru Ishii | 5 | 141 | 16.84 |
| Hager Gregory D | 6 | 1946 | 159.37 |
| Russell H. Taylor | 7 | 1970 | 438.00 |