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Steering second-order tensor voting by vote clustering
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-6827-9162
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-7750-1917
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0001-5765-2964
2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Among the various diffusion MRI techniques, diffusion tensor imaging (DTI) is still most commonly used in clinical practice in order to investigate connectivity and fibre anatomy in the human brain. Besides its apparent advantages of a short acquisition time and noise robustness compared to other techniques, it suffers from its major weakness of assuming a single fibre model in each voxel. This constitutes a problem for DTI fibre tracking algorithms in regions with crossing fibres. Methods approaching this problem in a postprocessing step employ diffusion-like techniques to correct the directional information. We propose an extension of tensor voting in which information from voxels with a single fibre is used to infer orientation distributions in multi fibre voxels. The method is able to resolve multiple fibre orientations by clustering tensor votes instead of adding them up. Moreover, a new vote casting procedure is proposed which is appropriate even for small neighbourhoods. To account for the locality of DTI data, we use a small neighbourhood for distributing information at a time, but apply the algorithm iteratively to close larger gaps. The method shows promising results in both synthetic cases and for processing DTI-data of the human brain.

Place, publisher, year, edition, pages
IEEE Computer Society, 2016. 1245-1248 p.
Keyword [en]
Tensor voting, spherical clustering, DTI
National Category
Medical Image Processing
Research subject
Medical Technology
Identifiers
URN: urn:nbn:se:kth:diva-190103DOI: 10.1109/ISBI.2016.7493492ISI: 000386377400294Scopus ID: 2-s2.0-84978401116ISBN: 978-1-4799-2349-6 (electronic)OAI: oai:DiVA.org:kth-190103DiVA: diva2:951382
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Funder
Swedish Research Council, 2012-3512
Note

QC 20160811

Available from: 2016-08-08 Created: 2016-08-08 Last updated: 2017-07-04Bibliographically approved

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Jörgens, DanielSmedby, ÖrjanMoreno, Rodrigo
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CiteExportLink to record
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Citation style
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  • harvard1
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  • vancouver
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More styles
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  • de-DE
  • en-GB
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  • Other locale
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Output format
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  • asciidoc
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