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Fully automatic brain segmentation using model-guided level set and skeleton based models
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-0442-3524
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
2013 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

Place, publisher, year, edition, pages
2013.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-115742OAI: oai:DiVA.org:liu-115742DiVA: diva2:796212
Conference
Grand Challenge on MR Brain Image Segmentation workshop" Nagoya, Japan, 2013.
Available from: 2015-03-18 Created: 2015-03-18 Last updated: 2015-04-01

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Wang, ChunliangSmedby, Örjan

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Wang, ChunliangSmedby, Örjan
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Division of Radiological SciencesFaculty of Health SciencesCenter for Medical Image Science and Visualization (CMIV)Department of Radiology in Linköping
Computer Vision and Robotics (Autonomous Systems)

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf