Volume Analysis Using Multimodal Surface Similarity

Martin Haidacher, Stefan Bruckner, Meister Eduard Gröller
Volume Analysis Using Multimodal Surface Similarity
IEEE Transactions on Visualization and Computer Graphics, 17(12):1969-1978, October 2011. [ Paper]
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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.

Additional Files and Images

Additional images and videos:
FastForward FastForward: Video of the Fast Forward Presentation
Video Video: Video demonstration of some of the key aspects of our approach
Additional files:
Presentation Presentation: VisWeek 2011 Presentation Slides
More files:
Paper
Paper




BibTeX

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@article{haidacher-2011-VAM,
  title =      "Volume Analysis Using Multimodal Surface Similarity",
  author =     "Martin Haidacher and Stefan Bruckner and Meister 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.",
  pages =      "1969--1978",
  month =      oct,
  number =     "12",
  event =      "IEEE Visualization 2011",
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "17",
  location =   "Providence, Rhode Island, USA",
  keywords =   "surface similarity, volume visualization, multimodal data",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2011/haidacher-2011-VAM/",
}