English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Thesis

Feature Extraction for Visual Analysis of DW-MRI Data

MPS-Authors
/persons/resource/persons45428

Schultz,  Thomas
Computer Graphics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Schultz, T. (2009). Feature Extraction for Visual Analysis of DW-MRI Data. PhD Thesis, Universität des Saarlandes, Saarbrücken.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-17C1-0
Abstract
Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) is a recent modality to investigate the major neuronal pathways of the human brain. However, the rich DW-MRI datasets cannot be interpreted without proper preprocessing. In order to achieve under- standable visualizations, this dissertation reduces the complex data to relevant features. The first part is inspired by topological features in flow data. Novel features reconstruct fuzzy fiber bundle geometry from probabilistic tractography results. The topological prop- erties of existing features that extract the skeleton of white matter tracts are clarified, and the core of regions with planar diffusion is visualized. The second part builds on methods from computer vision. Relevant boundaries in the data are identified via regularized eigenvalue derivatives, and boundary information is used to segment anisotropy isosurfaces into meaningful regions. A higher-order structure tensor is shown to be an accurate descriptor of local structure in diffusion data. The third part is concerned with fiber tracking. Streamline visualizations are improved by adding features from structural MRI in a way that emphasizes the relation between the two types of data, and the accuracy of streamlines in high angular resolution data is increased by modeling the estimation of crossing fiber bundles as a low-rank tensor approximation problem.