Name
Abstract | ||
|---|---|---|
We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate its utility in two very different settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease, |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33415-3_63 | MICCAI |
Keywords | Field | DocType |
novel method,hierarchy level,brain image,different setting,hierarchical manifold learning,regional correlation,discriminative region,image patch,neurodegenerative disease,regional variation | Computer science,Image processing,Automation,Software,Artificial intelligence,Granularity,Nonlinear dimensionality reduction,Hierarchy,Discriminative model,Manifold,Computer vision,Pattern recognition,Machine learning | Conference |
Volume | Issue | ISSN |
15 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
8 | 0.61 | 14 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanwal K Bhatia | 1 | 190 | 14.78 |
| Anil Rao | 2 | 299 | 34.89 |
| Anthony N Price | 3 | 253 | 15.32 |
| Robin Wolz | 4 | 661 | 34.42 |
| Jo Hajnal | 5 | 1796 | 119.03 |
| Daniel Rueckert | 6 | 9338 | 637.58 |