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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2010-11-24
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pubmed:abstractText |
Automatic or semi-automatic segmentation and tracking of artery trees from computed tomography angiography (CTA) is an important step to improve the diagnosis and treatment of artery diseases, but it still remains a significant challenging problem. In this paper, we present an artery extraction method to address the challenge. The proposed method consists of two steps: (1) a geometric moments based tracking to secure a rough centerline, and (2) a fully automatic generalized cylinder structure-based snake method to refine the centerlines and estimate the radii of the arteries. In this method, a new line direction based on first and second order geometric moments is adopted while both gradient and intensity information are used in the snake model to improve the accuracy. The approach has been evaluated on synthetic images as well as 8 clinical coronary CTA images with 32 coronary arteries. Our method achieves 94.7% overlap tracking ability within an average distance inside the vessel of 0.36 mm.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-10628944,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-10972323,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-11524226,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-11989849,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-15589093,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-15957092,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-16915122,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-18375175,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-18467061,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-19632885,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21096589-7724764
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1557-170X
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pubmed:author |
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pubmed:issnType |
Print
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pubmed:volume |
2010
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
3133-7
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pubmed:dateRevised |
2011-7-28
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pubmed:meshHeading |
pubmed-meshheading:21096589-Algorithms,
pubmed-meshheading:21096589-Artificial Intelligence,
pubmed-meshheading:21096589-Coronary Angiography,
pubmed-meshheading:21096589-Coronary Artery Disease,
pubmed-meshheading:21096589-Humans,
pubmed-meshheading:21096589-Pattern Recognition, Automated,
pubmed-meshheading:21096589-Radiographic Image Enhancement,
pubmed-meshheading:21096589-Radiographic Image Interpretation, Computer-Assisted,
pubmed-meshheading:21096589-Reproducibility of Results,
pubmed-meshheading:21096589-Sensitivity and Specificity,
pubmed-meshheading:21096589-Tomography, X-Ray Computed
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pubmed:year |
2010
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pubmed:articleTitle |
Coronary artery segmentation using geometric moments based tracking and snake-driven refinement.
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pubmed:affiliation |
State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, China.
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pubmed:publicationType |
Journal Article
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