Guided Volume Editing based on Histogram Dissimilarity

Information

• Publication Type: Journal Paper with Conference Talk
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: May 2015
• Journal: Computer Graphics Forum
• Volume: 34
• Number: 3
• Location: Cagliary, Italy
• Lecturer: Alexey Karimov
• Event: Eurographics Conference on Visualization (EuroVis)
• Conference date: 25. May 2015 – 29. May 2015
• Pages: 91 – 100
• Keywords: Edge and feature detection, Image Processing and Computer Vision, Computer Graphics, Display algorithms, Picture/Image Generation, Segmentation, Methodology and Techniques, Interaction techniques

Abstract

Segmentation of volumetric data is an important part of many analysis pipelines, but frequently requires manual inspection and correction. While plenty of volume editing techniques exist, it remains cumbersome and error-prone for the user to find and select appropriate regions for editing. We propose an approach to improve volume editing by detecting potential segmentation defects while considering the underlying structure of the object of interest. Our method is based on a novel histogram dissimilarity measure between individual regions, derived from structural information extracted from the initial segmentation. Based on this information, our interactive system guides the user towards potential defects, provides integrated tools for their inspection, and automatically generates suggestions for their resolution. We demonstrate that our approach can reduce interaction effort and supports the user in a comprehensive investigation for high-quality segmentations.

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BibTeX

@article{karimov-2015-HD,
  title =      "Guided Volume Editing based on Histogram Dissimilarity",
  author =     "Alexey Karimov and Gabriel Mistelbauer and Thomas Auzinger
               and Stefan Bruckner",
  year =       "2015",
  abstract =   "Segmentation of volumetric data is an important part of many
               analysis pipelines, but frequently requires manual
               inspection and correction. While plenty of volume editing
               techniques exist, it remains cumbersome and error-prone for
               the user to find and select appropriate regions for editing.
               We propose an approach to improve volume editing by
               detecting potential segmentation defects while considering
               the underlying structure of the object of interest. Our
               method is based on a novel histogram dissimilarity measure
               between individual regions, derived from structural
               information extracted from the initial segmentation. Based
               on this information, our interactive system guides the user
               towards potential defects, provides integrated tools for
               their inspection, and automatically generates suggestions
               for their resolution. We demonstrate that our approach can
               reduce interaction effort and supports the user in a
               comprehensive investigation for high-quality segmentations. ",
  month =      may,
  journal =    "Computer Graphics Forum",
  volume =     "34",
  number =     "3",
  pages =      "91--100",
  keywords =   "Edge and feature detection, Image Processing and Computer
               Vision, Computer Graphics, Display algorithms, Picture/Image
               Generation, Segmentation, Methodology and Techniques,
               Interaction techniques",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/",
}