Visualization in Medicine: Theory, Algorithms, and Applications
Visualization in Medicine: Theory, Algorithms, and Applications
Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data
Proceedings of the 30th DAGM symposium on Pattern Recognition
A viewer-dependent tensor field visualization using particle tracing
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
Flexible segmentation and smoothing of DT-MRI fields through a customizable structure tensor
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A viewer-dependent tensor field visualization using multiresolution and particle tracing
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part II
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The power of medical imaging modalities to measure and characterize biological tissue is amplified by visualization and analysis methods that help researchers to see and understand the structures within their data. Diffusion tensor magnetic resonance imaging can measure microstructural properties of biological tissue, such as the coherent linear organization of white matter of the central nervous system, or the fibrous texture of muscle tissue. This dissertation describes new methods for visualizing and analyzing the salient structure of diffusion tensor datasets. Glyphs from superquadric surfaces and textures from reaction-diffusion systems facilitate inspection of data properties and trends. Fiber tractography based on vector-tensor multiplication allows major white matter pathways to be visualized. The generalization of direct volume rendering to tensor data allows large-scale structures to be shaded and rendered. Finally, a mathematical framework for analyzing the derivatives of tensor values, in terms of shape and orientation change, enables analytical shading in volume renderings, and a method of feature detection important for feature-preserving filtering of tensor fields. Together, the combination of methods enhances the ability of diffusion tensor imaging to provide insight into the local and global structure of biological tissue.