Fabric-Like Visualization of Tensor Field Data on Arbitrary Surfaces in Image Space

  • Sebastian Eichelbaum
  • Mario Hlawitschka
  • Bernd Hamann
  • Gerik Scheuermann
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Tensors are of great interest to many applications in engineering and in medical imaging, but a proper analysis and visualization remains challenging. It already has been shown that, by employing the metaphor of a fabric structure, tensor data can be visualized precisely on surfaces where the two eigendirections in the plane are illustrated as thread-like structures. This leads to a continuous visualization of most salient features of the tensor data set. We introduce a novel approach to compute such a visualization from tensor field data that is motivated by image space line integral convolution (LIC). Although our approach can be applied to arbitrary, non-self-intersecting surfaces, the main focus lies on special surfaces following important features, such as surfaces aligned to the neural pathways in the human brain. By adding a postprocessing step, we are able to enhance the visual quality of the results, which improves perception of the major patterns.


Fractional Anisotropy Image Space Tensor Field Current Pixel Projection Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Alfred Anwander and Thomas R. Knösche from the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, for providing the human brain image data sets, and for fruitful discussions and comments, and Boris Jeremić, Department of Civil and Environmental Engineering, UC Davis, for providing the earthquake data set. We thank the members of the Visualization and Computer Graphics Research Group of the Institute for Data Analysis and Visualization, Department of Computer Science, UC Davis, and the members of the Abteilung für Bild- und Signalverarbeitung des Instituts für Informatik der Universität Leipzig, Germany.

This work has been supported in part by NSF grant CCF-0702817.


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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Sebastian Eichelbaum
    • 1
  • Mario Hlawitschka
    • 2
  • Bernd Hamann
    • 3
  • Gerik Scheuermann
    • 1
  1. 1.Abteilung für Bild- und Signalverarbeitung, Institut für InformatikUniversität LeipzigLeipzigGermany
  2. 2.Scientific Visualization Group, Institut für InformatikUniversität LeipzigLeipzigGermany
  3. 3.Department of Computer Science, Institute for Data Analysis and Visualization (IDAV)University of CaliforniaDavisUSA

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