Abstract
Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 459-466 |
| Number of pages | 8 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 6892 LNCS |
| Issue number | PART 2 |
| DOIs | |
| State | Published - 2011 |
| Externally published | Yes |
| Event | 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada Duration: Sep 18 2011 → Sep 22 2011 |
Keywords
- machine learning
- pattern based morphometry
- voxel based morphometry
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)