Linked Life Data

15202105

Source:http://linkedlifedata.com/resource/pubmed/id/15202105

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2004-6-17
pubmed:abstractText
We introduce a hybrid method for functional magnetic resonance imaging (fMRI) activation detection based on the well-developed split-merge and region-growing techniques. The proposed method includes conjoining both of the spatio-temporal priors inherent in split-merge and the prior information afforded by the hypothesis-led component of region selection. Compared to the fuzzy c-means clustering analysis, this method avoids making assumptions about the number of clusters and the computation complexity is reduced markedly. We evaluated the effectiveness of the proposed method in comparison with the general linear model and the fuzzy c-means clustering method conducted on simulated and in vivo datasets. Experimental results show that our method successfully detected expected activated regions and has advantages over the other two methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1065-9471
pubmed:author
pubmed:copyrightInfo
Copyright 2004 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
271-9
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
A split-merge-based region-growing method for fMRI activation detection.
pubmed:affiliation
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China.
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't