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

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 representation. Nevertheless, its application and usability remains limited due to its computational expensive and sensitive nature. We introduce a novel approach to compute a fabric-like texture pattern from tensor 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 rendering approachwhosemain 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. 1 Motivation and Related Work Since the introduction of tensor lines and hyperstreamlines [5], there have been many research efforts directed at the continuous representation of tensor fields, including research on tensor field topology [11, 24, 23]. Zheng and Pang introduced HyperLIC [31], which makes it possible to display a single eigendirection of a tensor Sebastian Eichelbaum · Gerik Scheuermann Abteilung für Bildund Signalverarbeitung, Institut für Informatik, Universität Leipzig, Germany, e-mail: {eichelbaum | scheuermann }@informatik.uni-leipzig.de Mario Hlawitschka and Bernd Hamann Institute for Data Analysis and Visualization (IDAV), Department of Computer Science, University of California, Davis, CA, e-mail: {hlawitschka | hamann}@ucdavis.edu