Volume Analysis Using Multimodal Surface Similarity

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: October 2011
• Journal: IEEE Transactions on Visualization and Computer Graphics
• Volume: 17
• Number: 12
• Location: Providence, Rhode Island, USA
• Lecturer: Martin Haidacher
• Event: IEEE Visualization 2011
• Conference date: 23. October 2011 – 28. October 2011
• Pages: 1969 – 1978
• Keywords: surface similarity, volume visualization, multimodal data

Abstract

The combination of volume data acquired by multiple modalities has been recognized as an important but challenging task. Modalities often differ in the structures they can delineate and their joint information can be used to extend the classification space. However, they frequently exhibit differing types of artifacts which makes the process of exploiting the additional information non-trivial. In this paper, we present a framework based on an information-theoretic measure of isosurface similarity between different modalities to overcome these problems. The resulting similarity space provides a concise overview of the differences between the two modalities, and also serves as the basis for an improved selection of features. Multimodal classification is expressed in terms of similarities and dissimilarities between the isosurfaces of individual modalities, instead of data value combinations. We demonstrate that our approach can be used to robustly extract features in applications such as dual energy computed tomography of parts in industrial manufacturing.

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BibTeX

@article{haidacher-2011-VAM,
  title =      "Volume Analysis Using Multimodal Surface Similarity",
  author =     "Martin Haidacher and Stefan Bruckner and Eduard Gr\"{o}ller",
  year =       "2011",
  abstract =   "The combination of volume data acquired by multiple
               modalities has been recognized as an important but
               challenging task. Modalities often differ in the structures
               they can delineate and their joint information can be used
               to extend the classification space. However, they frequently
               exhibit differing types of artifacts which makes the process
               of exploiting the additional information non-trivial. In
               this paper, we present a framework based on an
               information-theoretic measure of isosurface similarity
               between different modalities to overcome these problems. The
               resulting similarity space provides a concise overview of
               the differences between the two modalities, and also serves
               as the basis for an improved selection of features.
               Multimodal classification is expressed in terms of
               similarities and dissimilarities between the isosurfaces of
               individual modalities, instead of data value combinations.
               We demonstrate that our approach can be used to robustly
               extract features in applications such as dual energy
               computed tomography of parts in industrial manufacturing.",
  month =      oct,
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "17",
  number =     "12",
  pages =      "1969--1978",
  keywords =   "surface similarity, volume visualization, multimodal data",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/haidacher-2011-VAM/",
}