Tensorlines: advection-diffusion based propagation through diffusion tensor fields
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
The Topology of Symmetric, Second-Order 3D Tensor Fields
IEEE Transactions on Visualization and Computer Graphics
The topology of symmetric, second-order tensor fields
VIS '94 Proceedings of the conference on Visualization '94
Topological Lines in 3D Tensor Fields
VIS '04 Proceedings of the conference on Visualization '04
Topological Lines in 3D Tensor Fields and Discriminant Hessian Factorization
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Anisotropy creases delineate white matter structure in diffusion tensor MRI
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Level Set Segmentation With Multiple Regions
IEEE Transactions on Image Processing
Generation of an Importance Map for Visualized Images
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
New Scalar Measures for Diffusion-Weighted MRI Visualization
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Proceedings of the Symposium on Eye Tracking Research and Applications
Abstractive representation and exploration of hierarchically clustered diffusion tensor fiber tracts
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
Topological features in 2D symmetric higher-order tensor fields
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Closed stream lines in uncertain vector fields
Proceedings of the 27th Spring Conference on Computer Graphics
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Topological methods give concise and expressive visual representations of flow fields. The present work suggests acomparable method for the visualization of human brain diffusion MRI data. We explore existing techniques for the topological analysis of generic tensor fields, but find them inappropriate for diffusion MRI data. Thus, we propose a novel approach that considers the asymptotic behavior of a probabilistic fiber tracking method and define analogs of the basic concepts of flow topology, like critical points, basins, and faces, with interpretations in terms of brain anatomy. The resulting features are fuzzy, reflecting the uncertainty inherent in any connectivity estimate from diffusion imaging. We describe an algorithm to extract the new type of features, demonstrate its robustness under noise, and present results for two regions in a diffusion MRI dataset to illustrate that the method allows a meaningful visual analysis of probabilistic fiber tracking results.