Feature Extraction for Visual Analysis of DW-MRI Data

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 understandable 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 properties 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.