Linked Life Data

14663959

Source:http://linkedlifedata.com/resource/pubmed/id/14663959

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2003-12-10
pubmed:abstractText
Segmentation of positron emission tomography (PET) images is a difficult task. In this study, we propose a new method for delineation of brain structures according to the tracer uptake. The method is based on a new deformable model which is particularly designed for extracting surfaces automatically from noisy images. The automation is achieved by using a global optimization algorithm for minimizing the energy of the deformable model. As an example, the coarse cortical structure was extracted from FDG PET brain images by delineating first the brain surface and then the white matter surface. We have tested the method with the image of the brain phantom and images from a small number (N = 17) of FDG brain studies. The cortical structure was automatically and reliably found from all the images. The proposed method provides new opportunities for automatic and repeatable structure extraction applicable for regional quantification of the tracer uptake.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
T
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
95
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
33-8
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Delineation of brain structures from positron emission tomography images with deformable models.
pubmed:affiliation
Department of Computer and Information Sciences, University of Tampere, Finland. jm@cs.uta.fi
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't