Name
Abstract | ||
|---|---|---|
For segmenting complex structures like vertebrae, a priori knowledge by means of statistical shape models (SSMs) is often incorporated. One of the main challenges using SSMs is the solution of the correspondence problem. In this work we present a generic automated approach for solving the correspondence problem for vertebrae. We determine two closed loops on a reference shape and propagate them consistently to the remaining shapes of the training set. Then every shape is cut along these loops and parameterized to a rectangle. There, we optimize a novel combined energy to establish the correspondences and to reduce the unavoidable area and angle distortion. Finally, we present an adaptive resampling method to achieve a good shape representation. A qualitative and quantitative evaluation shows that using our method we can generate SSMs of higher quality than the ICP approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23629-7_61 | MICCAI (2) |
Keywords | Field | DocType |
good shape representation,remaining shape,closed loop,adaptive resampling method,correspondence problem,generic automated approach,automatic construction,icp approach,angle distortion,statistical shape model,reference shape | Training set,Computer vision,Parameterized complexity,Pattern recognition,Computer science,Rectangle,A priori and a posteriori,Minimum description length,Artificial intelligence,Correspondence problem,Distortion,Resampling | Conference |
Volume | Issue | ISSN |
14 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
3 | 0.39 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meike Becker | 1 | 26 | 2.68 |
| matthias kirschner | 2 | 84 | 10.50 |
| Simon Fuhrmann | 3 | 157 | 8.62 |
| Stefan Wesarg | 4 | 218 | 40.03 |