Name
Abstract | ||
|---|---|---|
Groupwise non-rigid image registration plays an important role in medical image analysis. As local optimisation is largely used in such techniques, a good initialisation is required to avoid local minima. Although the traditional approach to initialisation--affine transformation--generally works well, recent studies have shown that it is inadequate when registering images of complex structures. In this paper we present a more sophisticated method that uses the sparse matches of a parts+geometry model as the initialisation. The choice of parts is made by a voting scheme. We generate a large number of candidate parts, randomly construct many different parts+geometry models and then use the models to select the parts with good localisability. We show that the algorithm can achieve better results than the state of the art on three different datasets of increasing difficulty. We also show that dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22092-0_52 | IPMI |
Keywords | Field | DocType |
annotate new image,local optimisation,automatic part selection,good initialisation,local minimum,good localisability,different part,geometry model,groupwise non-rigid image registration,groupwise registration process,different datasets | Computer vision,Voting,Pattern recognition,Computer science,Minimum description length,Maxima and minima,Active appearance model,Artificial intelligence,Image registration | Conference |
Volume | ISSN | Citations |
22 | 1011-2499 | 4 |
PageRank | References | Authors |
0.41 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei Zhang | 1 | 10 | 0.89 |
| Timothy F. Cootes | 2 | 4358 | 579.15 |