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Skeleton-based fast, fully automated generation of vessel tree structure for clinical evaluation of blood vessel systems
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-0442-3524
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2017 (English)In: Skeletonization: Theory, Methods and Applications, Elsevier, 2017, 345-382 p.Chapter in book (Other academic)
Abstract [en]

This chapter focuses on skeleton detection for clinical evaluation of blood vessel systems. In clinical evaluation, there is a need for fast and accurate segmentation algorithms that can reliably provide vessel measurements and additional information for clinicians to decide the diagnosis.Since blood vessels have a characteristic tubular shape, their segmentation can be accelerated and facilitated by first identifying the rough vessel centerlines, which can be seen as a special case of an image skeleton extraction algorithm. A segmentation algorithm will finally use the resulting skeleton as a seed region during the segmentation. The proposed method takes an unprocessed 3D computed tomography angiography (CTA) scan as an input and generates a connected graph of centrally located arterial voxels. The method works in two levels, where large arteries are captured in the first level, and small arteries are added in the second one. Experimental results show that the method can achieve high overlap rate and acceptable detection rate accuracies. High computational efficiency of the method opens the possibility for an interactive clinical use.

Place, publisher, year, edition, pages
Elsevier, 2017. 345-382 p.
Keyword [en]
Anatomy-based analysis, Artery nodes connection, Artery nodes detection, Blood vessel systems, Computed tomography angiography, Skeleton extraction, Vascular segmentation, Vessel tree structure
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-218488DOI: 10.1016/B978-0-08-101291-8.00014-6Scopus ID: 2-s2.0-85032157152ISBN: 9780081012925 ISBN: 9780081012918 OAI: oai:DiVA.org:kth-218488DiVA: diva2:1161096
Note

QC 20171129

Available from: 2017-11-29 Created: 2017-11-29 Last updated: 2017-11-29Bibliographically approved

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Wang, Chunliang

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf