Image-space Tensor Field Visualization Using a LIC-like Method

Eichelbaum, Sebastian
Hlawitschka, Mario
Hamann, Bernd
Scheuermann, Gerik
Abstract in English: 
Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. Physics-based visualization of tensor fields has proven to show the main features of symmetric second-order tensor fields, while still displaying the most important information of the data, namely the main directions in medical diffusion tensor data using texture and additional attributes using color-coding, in a continuous represen- tation. Nevertheless, its application and usability remains limited due to its compu- tational expensive and sensitive nature. We introduce a novel approach to compute a fabric-like texture pattern from ten- sor fields motivated by image-space line integral convolution (LIC). Although, our approach can be applied to arbitrary, non-selfintersecting surfaces, we are focusing on special surfaces following neural fibers in the brain. We employ a multi-pass ren- dering approach whose main focus lies on regaining three-dimensionality of the data under user interaction as well as being able to have a seamless transition between local and global structures including a proper visualization of degenerated points.
Notes / Bemerkungen: 
To be published in Proceedings of the 2009 Workshop on "Visualization in Medicine and Life Sciences" (VMLS 09)
paper.pdf7.77 MB
presentation.pdf7.32 MB