Name
Abstract | ||
|---|---|---|
We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains, but instead focuses on modeling and discriminating between the cortical structural patterns. We demonstrate our approach on 120 subject brains from an Alzheimer's dataset, and present applications to clustering, classification, and dimension reduction. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ISBI.2011.5872597 | 2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO |
Keywords | Field | DocType |
cortical distributions, Jensen-Shannon divergence, clustering, dimension reduction | Pairwise comparison,Dimensionality reduction,Neurophysiology,Pattern recognition,Computer science,Jensen–Shannon divergence,Gaussian,Artificial intelligence,Neuroimaging,Cluster analysis,Pattern matching | Conference |
ISSN | Citations | PageRank |
1945-7928 | 5 | 1.00 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shantanu H. Joshi | 1 | 845 | 50.12 |
| Ian Bowman | 2 | 12 | 3.18 |
| Arthur W. Toga | 3 | 3128 | 261.46 |
| John D Van Horn | 4 | 316 | 28.50 |