Linked Life Data

20426193

Source:http://linkedlifedata.com/resource/pubmed/id/20426193

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2010-4-29
pubmed:abstractText
The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
869-76
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
ECOC random fields for lumen segmentation in radial artery IVUS sequences.
pubmed:affiliation
Dep. of Applied Mathematics and Analysis, University of Barcelona, Spain. f.ciompi@cvc.uab.es
pubmed:publicationType
Journal Article