Information

Also available in TR-186-2-08-04.

Abstract

Transfer functions are an essential part of volume visualization. In multimodal visualization at least two values exist at every sample point. Additionally, other parameters, such as gradient magnitude, are often retrieved for each sample point. To find a good transfer function for this high number of parameters is challenging because of the complexity of this task. In this paper we present a general information-based approach for transfer function design in multimodal visualization which is independent of the used modality types. Based on information theory, the complex multi-dimensional transfer function space is fused to allow utilization of a well-known 2D transfer function with a single value and gradient magnitude as parameters. Additionally, a quantity is introduced which enables better separation of regions with complementary information. The benefit of the new method in contrast to other techniques is a transfer function space which is easy to understand and which provides a better separation of different tissues. The usability of the new approach is shown on examples of different modalities.

Additional Files and Images

Additional images and videos:
CT-PET CT-PET: Multimodal Visualization of Neck Tumor
Additional files:
Paper Paper: Publication from VCBM 2008
Presentation Presentation: Slides of Presentation at VCBM

BibTeX

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@inproceedings{haidacher-2008-vcbm,
  title =      "Information-based Transfer Functions for Multimodal
               Visualization",
  author =     "Martin Haidacher and Stefan Bruckner and Armin Kanitsar and
               Meister Eduard Gr{\"o}ller",
  year =       "2008",
  abstract =   "Transfer functions are an essential part of volume
               visualization. In multimodal visualization at least two
               values exist at every sample point. Additionally, other
               parameters, such as gradient magnitude, are often retrieved
               for each sample point. To find a good transfer function for
               this high number of parameters is challenging because of the
               complexity of this task. In this paper we present a general
               information-based approach for transfer function design in
               multimodal visualization which is independent of the used
               modality types. Based on information theory, the complex
               multi-dimensional transfer function space is fused to allow
               utilization of a well-known 2D transfer function with a
               single value and gradient magnitude as parameters.
               Additionally, a quantity is introduced which enables better
               separation of regions with complementary information. The
               benefit of the new method in contrast to other techniques is
               a transfer function space which is easy to understand and
               which provides a better separation of different tissues. The
               usability of the new approach is shown on examples of
               different modalities.",
  pages =      "101--108",
  month =      oct,
  booktitle =  "VCBM ",
  editor =     "C.P Botha, G. Kindlmann, W.J. Niessen, and B. Preim",
  isbn =       "978-3-905674-13-2",
  issn =       "2070-5778",
  publisher =  "Eurographics Association",
  location =   "Delft",
  keywords =   "Multimodal Visualization, Transfer Function, Information
               Theory",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2008/haidacher-2008-vcbm/",
}