Linked Life Data

16275137

Source:http://linkedlifedata.com/resource/pubmed/id/16275137

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2006-3-27
pubmed:abstractText
This paper proposes a temporally consistent and spatially adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging, or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1053-8119
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
388-99
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
CLASSIC: consistent longitudinal alignment and segmentation for serial image computing.
pubmed:affiliation
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, PA 19104, USA. zhong.xue@uphs.upenn.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural