Article Figures & Data
Figures
- Fig 1.
The most important shape information is automatically extracted and used for clustering. A, Image shows the input fiber tracts. B, Image shows the clustering step. Each point in this image represents the similarity relationships of 1 fiber (these points come from the highest eigenvectors of the similarity matrix in a process called “spectral embedding”). C, Image shows the tracts are colored by cluster.
- Fig 2.
Fiber tract clustering in the fornix. A fiber path was seeded in each voxel of the single region of interest, which can be seen in image A. These fibers are shown in image B. Next the clustering method was applied to separate the fiber paths into 2 clusters, the left (red) and right (blue) fornices, as shown in image C. Images D–F show similar clustering results for additional subjects.
- Fig 3.
Fiber paths were seeded in each voxel of initial seed regions of interest located in the temporal stem. The clustering was then performed (A, B) to create 2 groups of paths, corresponding to the inferior occipitofrontal fasciculus and the uncinate fasciculus.
- Fig 4.
Fiber tract clustering in the corpus callosum. Using a fractional anisotropy map (A), we drew a region of interest on the corpus callosum (B).Because fiber paths have similar shapes (C), clustering produces an inadequate result (D). If the user isolates a region of the corpus callosum containing fiber paths of different shapes, clustering is successful (E).
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