Publications

Guided Volume Editing based on Histogram Dissimilarity

A. Karimov, G. Mistelbauer, T. Auzinger, and S. Bruckner

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.

A. Karimov, G. Mistelbauer, T. Auzinger, and S. Bruckner, "Guided Volume Editing based on Histogram Dissimilarity," Computer Graphics Forum, vol. 34, iss. 3, p. 91–100, 2015. doi:10.1111/cgf.12621
[BibTeX]

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.
@ARTICLE {Karimov-2015-GVE,
author = "Alexey Karimov and Gabriel Mistelbauer and Thomas Auzinger and Stefan Bruckner",
title = "Guided Volume Editing based on Histogram Dissimilarity",
journal = "Computer Graphics Forum",
year = "2015",
volume = "34",
number = "3",
pages = "91--100",
month = "may",
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.",
pdf = "pdfs/Karimov-2015-GVE.pdf",
images = "images/Karimov-2015-GVE.jpg",
thumbnails = "images/Karimov-2015-GVE.png",
youtube = "https://www.youtube.com/watch?v=zjTYkXTm_dM",
doi = "10.1111/cgf.12621",
event = "EuroVis 2015",
keywords = "medical visualization, segmentation, volume editing, interaction",
location = "Cagliari, Italy",
owner = "bruckner",
timestamp = "2015.06.08",
url = "//www.cg.tuwien.ac.at/research/publications/2015/karimov-2015-HD/"
}
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