Linked Life Data

16686034

Source:http://linkedlifedata.com/resource/pubmed/id/16686034

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2006-5-11
pubmed:abstractText
We present a novel technique for the automatic formation of vascular trees from segmented tubular structures. Our method combines a minimum spanning tree algorithm with a minimization criterion of the Mahalanobis distance. First, a multivariate class of connected junctions is defined using a set of trained vascular trees and their corresponding image volumes. Second, a minimum spanning tree algorithm forms the tree using the Mahalanobis distance of each connection from the "connected" class as a cost function. Our technique allows for the best combination of the discrimination criteria between connected and non-connected junctions and is also modality, organ and segmentation specific.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
806-12
pubmed:dateRevised
2011-9-22
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Automatic vascular tree formation using the Mahalanobis distance.
pubmed:affiliation
Computer-Aided Diagnosis and Display Lab, Department of Radiology, The University of North Carolina at Chapel Hill, 27510 Chapel Hill, USA. jomier@unc.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies, Research Support, N.I.H., Extramural