Information-based Transfer Functions for Multimodal Visualization

Information

• Replaced by: haidacher-2008-vcbm
• Publication Type: Technical Report
• Workgroup(s)/Project(s):
  • DiagVis: Diagnostic Visualization for Medical Applications
• Date: April 2008
• Number: TR-186-2-08-04
• Keywords: Information-based classification, Information theory, Point-wise mutual information, Transfer functions, Multimodal visualization

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 a well-known 2D transfer function with a single value and gradient magnitude as parameters. Additionally, a quantity is introduced which is used for achieving 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.

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BibTeX

@techreport{TR-186-2-08-04,
  title =      "Information-based Transfer Functions for Multimodal
               Visualization",
  author =     "Martin Haidacher and Stefan Bruckner and Armin Kanitsar and
               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 a
               well-known 2D transfer function with a single value and
               gradient magnitude as parameters. Additionally, a quantity
               is introduced which is used for achieving 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.",
  month =      apr,
  number =     "TR-186-2-08-04",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  institution = "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  note =       "human contact: technical-report@cg.tuwien.ac.at",
  keywords =   "Information-based classification, Information theory,
               Point-wise mutual information, Transfer functions,
               Multimodal visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2008/TR-186-2-08-04/",
}