Guided Volume Editing based on Histogram Dissimilarity

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 errorprone for the user to find and select appropriate regions for editing. We propose...

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Vydáno v:	Computer graphics forum Ročník 34; číslo 3; s. 91 - 100
Hlavní autoři:	Karimov, A., Mistelbauer, G., Auzinger, T., Bruckner, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Blackwell Publishing Ltd 01.06.2015
Témata:	Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Defects Editing Histograms I.3.3 [Computer Graphics]: Picture/Image Generation-Display algorithms I.3.6 [Computer Graphics]: Methodology and Techniques-Interaction techniques I.4.6 [Image Processing and Computer Vision]: Segmentation-Edge and feature detection Image processing systems Inspection Interactive Manuals Pipelines Segmentation Studies Volumetric analysis
ISSN:	0167-7055, 1467-8659
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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 errorprone 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.
AbstractList	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 errorprone 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.
Author	Mistelbauer, G. Karimov, A. Bruckner, S. Auzinger, T.
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Cites_doi	10.1145/235815.235821 10.1016/j.cag.2006.11.015 10.1109/TVCG.2014.2346371 10.1109/PACIFICVIS.2010.5429615 10.1007/s11263-006-7934-5 10.1117/12.481097 10.1145/218380.218442 10.1007/978-3-540-85988-8_36 10.1007/978-3-642-18421-5_20 10.1109/ISBI.2013.6556670 10.1111/cgf.12193 10.1007/11866763_109 10.1111/cgf.12133 10.1111/cgf.12110 10.1109/TMI.2009.2013851 10.1117/1.JMI.1.3.034005 10.1109/TVCG.2010.208
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Copyright	2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2015 The Eurographics Association and John Wiley & Sons Ltd.
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Journal of Medical Imaging 1, 3 (2014), 034005-01-034005-16. 2 – reference: Heimann T., van Ginneken B., Styner M., Arzhaeva Y., Aurich V., Bauer C., Beck A., Becker C., Beichel R., Bekes G., Bello F., Binnig G.K., Bischof H., Bornik A., Cashman P., Chi Y., Cordova A., Dawant B.M., Fidrich M., Furst J.D., Furukawa D., Grenacher L., Hornegger J., Kainmüller D., Kitney R., Kobatake H., Lamecker H., Lange T., Lee J., Lennon B., Li R., Li S., Meinzer H.-P., Németh G., Raicu D.S., Rau A.-M., van Rikxoort E.M., Rousson M., Ruskó L., Saddi K.A., Schmidt G., Seghers D., Shimizu A., Slagmolen P., Sorantin E., Soza G., Susomboon R., Waite J.M., Wimmer A., Wolf I.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imag. 28, 8 (2009), 1251-1265. 8 – reference: Reniers D.: Skeletonization and Segmentation of Binary Voxel Shapes. 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Graph. Forum – volume: 56 start-page: 462 year: 1994 end-page: 478 article-title: Building skeleton models via 3D medial surface/axis thinning algorithms publication-title: Lecture Notes in Comput. Sci – volume: 6533 start-page: 204 year: 2011 end-page: 213 article-title: Spotlight: Automated confidence‐based user guidance for increasing efficiency in interactive 3D image segmentation publication-title: Lecture Notes in Comput. Sci – volume: 3 start-page: 161 year: 2003 end-page: 169 article-title: Segmentation of Tomographic Data by Hierarchical Watershed Transform publication-title: Journal of Medical Informatics and Technologies – volume: 20 start-page: 2466 issue: 12 year: 2014 end-page: 2475 article-title: Design and Evaluation of Interactive Proofreading Tools for Connectomics publication-title: IEEE Trans. Vis. Comput. Graphics – volume: 5032 start-page: 643 year: 2003 end-page: 653 article-title: IWT – Interactive Watershed Transform: A hierarchical method for efficient interactive and automated segmentation of multidimensional grayscale images publication-title: Proc. SPIE Med. Imaging – start-page: 187 year: 2007 end-page: 194 – year: 2014 – volume: 32 start-page: 231 year: 2013 end-page: 240 article-title: Vessel Visualization using Curvicircular Feature Aggregation publication-title: Comput. Graph. Forum – volume: 574 start-page: 420 year: 2010 end-page: 424 article-title: Interaktionstechniken zur Korrektur medizinischer 3D‐Segmentierungen publication-title: Bildverarbeitung für die Medizin – year: 2012 – volume: 4191 start-page: 888 year: 2006 end-page: 95 article-title: An Energy Minimization Approach to the Data Driven Editing of Presegmented Images/Volumes publication-title: Lecture Notes in Comput. Sci – volume: 1 issue: 3 year: 2014 article-title: On the evaluation of segmentation editing tools publication-title: Journal of Medical Imaging – start-page: 78 year: 1998 end-page: 85 – volume: 28 start-page: 1251 issue: 8 year: 2009 end-page: 1265 article-title: Comparison and evaluation of methods for liver segmentation from CT datasets publication-title: IEEE Trans. Med. Imag – start-page: 191 year: 1995 end-page: 198 – volume: 5241 start-page: 296 year: 2008 end-page: 304 article-title: Interactive separation of segmented bones in CT volumes using graph cut publication-title: Lecture Notes in Comput. Sci – volume: 32 start-page: 144 issue: 8 year: 2013 end-page: 157 article-title: Sketch‐Based Editing Tools for Tumour Segmentation in 3D Medical Images publication-title: Comput. Graph. Forum – volume: 16 start-page: 1358 issue: 6 year: 2010 end-page: 65 article-title: Uncertainty‐Aware Guided Volume Segmentation publication-title: IEEE Trans. Vis. Comput. Graphics – volume: 70 start-page: 109 issue: 2 year: 2006 end-page: 131 article-title: Graph cuts and efficient N‐D image segmentation publication-title: Int. J. Comput. Vision – start-page: 17 year: 2010 end-page: 24 – volume: 22 start-page: 469 issue: 4 year: 1996 end-page: 483 article-title: The quickhull algorithm for convex hulls publication-title: ACM Trans. Math. 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SubjectTerms	Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Defects Editing Histograms I.3.3 [Computer Graphics]: Picture/Image Generation-Display algorithms I.3.6 [Computer Graphics]: Methodology and Techniques-Interaction techniques I.4.6 [Image Processing and Computer Vision]: Segmentation-Edge and feature detection Image processing systems Inspection Interactive Manuals Pipelines Segmentation Studies Volumetric analysis
Title	Guided Volume Editing based on Histogram Dissimilarity
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