Linked Life Data

17354737

Source:http://linkedlifedata.com/resource/pubmed/id/17354737

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-3-14
pubmed:abstractText
Multi-figure m-reps allow us to represent and analyze a complex anatomical object by its parts, by relations among its parts, and by the object itself as a whole entity. This representation also enables us to gather either global or hierarchical statistics from a population of such objects. We propose a framework to train the statistics of multi-figure anatomical objects from real patient data. This training requires fitting multi-figure m-reps to binary characteristic images of training objects. To evaluate the fitting approach, we propose a Monte Carlo method sampling the trained statistics. It shows that our methods generate geometrically proper models that are close to the set of Monte Carlo generated target models and thus can be expected to yield similar statistics to that used for the Monte Carlo generation.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
701-12
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Multi-figure anatomical objects for shape statistics.
pubmed:affiliation
Medical Image Display and Analysis Group, University of North Carolina at Chapel Hill, NC 27599, USA. han@cs.unc.edu
pubmed:publicationType
Journal Article, Evaluation Studies, Research Support, N.I.H., Extramural