Name
Abstract | ||
|---|---|---|
In this paper, we propose a hybrid similarity measure for 2D-3D image registration that is a weighted combination of an intensity-based image similarity measure and a point-based measure incorporating a single fiducial marker. We evaluate its accuracy and robustness using gold-standard clinical spine image data. The use of one fiducial marker substantially improves registration accuracy and robustness. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/978-3-540-39899-8_36 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
3d imaging,image registration,gold standard | Computer vision,Fiducial marker,Pattern recognition,Similarity measure,Computer science,Robustness (computer science),Artificial intelligence,Image registration | Conference |
Volume | ISSN | Citations |
2878 | 0302-9743 | 3 |
PageRank | References | Authors |
0.75 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel B. Russakoff | 1 | 238 | 20.50 |
| Torsten Rohlfing | 2 | 208 | 20.57 |
| Ramin Shahidi | 3 | 149 | 14.55 |
| Daniel H. Kim | 4 | 19 | 2.28 |
| John R. Adler Jr. | 5 | 88 | 8.29 |
| Calvin R. Maurer Jr. | 6 | 888 | 90.04 |