Document Detail


Automatic Atlas-based Three-label Cartilage Segmentation from MR Knee Images.
MedLine Citation:
PMID:  23685704     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
This paper proposes a method to build a bone-cartilage atlas of the knee and to use it to automatically segment femoral and tibial cartilage from T1 weighted magnetic resonance (MR) images. Anisotropic spatial regularization is incorporated into a three-label segmentation framework to improve segmentation results for the thin cartilage layers. We jointly use the atlas information and the output of a probabilistic k nearest neighbor classifier within the segmentation method. The resulting cartilage segmentation method is fully automatic. Validation results on 18 knee MR images against manual expert segmentations from a dataset acquired for osteoarthritis research show good performance for the segmentation of femoral and tibial cartilage (mean Dice similarity coefficient of 78.2% and 82.6% respectively).
Authors:
Liang Shan; Cecil Charles; Marc Niethammer
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Proceedings / sponsored by IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence. Workshop on Mathematical Methods in Biomedical Image Analysis     Volume:  -     ISSN:  -     ISO Abbreviation:  Proc Workshop Math Methods Biomed Image Analysis     Publication Date:  2012  
Date Detail:
Created Date:  2013-5-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101525825     Medline TA:  Proc Workshop Math Methods Biomed Image Analysis     Country:  -    
Other Details:
Languages:  ENG     Pagination:  241-246     Citation Subset:  -    
Affiliation:
Department of Computer Science, UNC Chapel Hill, shan@cs.unc.edu.
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Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
R21 AR059890/AR/NIAMS NIH HHS; R21 AR059890-01A1/AR/NIAMS NIH HHS; R21 AR059890-02/AR/NIAMS NIH HHS

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