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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Veröffentlicht in:Computer graphics forum Jg. 34; H. 3; S. 91 - 100
Hauptverfasser: Karimov, A., Mistelbauer, G., Auzinger, T., Bruckner, S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.06.2015
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:Supporting InformationSupporting InformationSupporting Information
ArticleID:CGF12621
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12621