Name
Abstract | ||
|---|---|---|
Discovering and understanding relationship patterns within a feature-rich archive (e.g. quantifying the degree of neuroanatomical similarity between the scanned subjects of a Magnetic Resonance Imaging (MRI) repository) is a nontrivial task. Scientists and expert users employ a variety of commodity algorithms for automated statistical analysis of feature patterns within a collection. But such analysis assumes the user is an experienced statistician, and disregards human visual processing capability. In this work we define a visual process for exploring the structure, relationships and patterns within a neuroimaging archive. Through dataset placement, our three-dimensional environment expresses similarity among the data. The application facilitates further analysis via two-stage exploratory clustering and classification. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2037715.2037817 | SIGGRAPH Posters |
Keywords | Field | DocType |
magnetic resonance imaging,visual process,feature similarity space,feature-rich archive,visual navigation,disregards human visual processing,neuroimaging archive,neuroanatomical similarity,dataset placement,commodity algorithm,experienced statistician,automated statistical analysis,segmentation,three dimensional,clustering,magnetic resonance image,statistical analysis | Computer vision,Sequential data,Visual processing,Statistician,Segmentation,Computer science,Visual navigation,Artificial intelligence,Neuroimaging,Cluster analysis,Statistical analysis | Conference |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ian Bowman | 1 | 12 | 3.18 |
| Shantanu H. Joshi | 2 | 845 | 50.12 |
| John D Van Horn | 3 | 316 | 28.50 |