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2007
IEEE

Fiber Tract Clustering on Manifolds With Dual Rooted-Graphs

10 years 1 months ago
Fiber Tract Clustering on Manifolds With Dual Rooted-Graphs
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to generate this similarity measure, the chamfer or Hausdorff distance is initially employed as a local distance metric to construct minimum spanning trees between pairwise fiber tracts. These minimum spanning trees are effective in capturing the intrinsic geometry of the fiber tracts. Hence, they are used to capture the neighborhood structures of the fiber tract data set. We next assume the high-dimensional input fiber tracts to lie on low-dimensional non-linear manifolds. We apply Locally Linear Embedding, a popular manifold learning technique, to define a low-dimensional embedding of the fiber tracts that preserves the neighborhood structures of the high-dimensional data structure as captured by the method of dual-rooted graphs. Clustering is then performed on this low-dimensional data structure using the k-means...
Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Al
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Andy Tsai, Carl-Fredrik Westin, Alfred O. Hero, Alan S. Willsky
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