Name
Abstract | ||
|---|---|---|
Accurate automated segmentation of the right ventricle is difficult due in part to the large shape variation found between patients. We explore the ability of manifold learning based shape models to represent the complexity of shape variation found within an RV dataset as compared to a typical PCA based model. This is empirically evaluated with the manifold model displaying a greater ability to represent complex shapes. Furthermore, we present a combined manifold shape model and Markov Random Field Segmentation framework. The novelty of this method is the iterative generation of targeted shape priors from the manifold using image information and a current estimate of the segmentation; a process that can be seen as a traversal across the manifold. We apply our method to the independently evaluated MICCAI 2012 RV Segmentation Challenge data set. Our method performs similarly or better than the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10404-1_85 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Scale-space segmentation,Random field,Pattern recognition,Computer science,Markov random field,Segmentation,Markov chain,Artificial intelligence,Diffusion map,Nonlinear dimensionality reduction,Mixture model | Conference | 8673 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 3 |
PageRank | References | Authors |
0.40 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oliver Moolan-Feroze | 1 | 3 | 1.41 |
| Majid Mirmehdi | 2 | 955 | 96.94 |
| Mark C. K. Hamilton | 3 | 8 | 1.51 |
| Chiara Bucciarelli-Ducci | 4 | 3 | 0.73 |