Linked Life Data

14523954

Source:http://linkedlifedata.com/resource/pubmed/id/14523954

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2003-10-2
pubmed:abstractText
Fully automated methods for analyzing MR spectra would be of great benefit for clinical diagnosis, in particular for the extraction of relevant information from large databases for subsequent pattern recognition analysis. Independent component analysis (ICA) provides a means of decomposing signals into their constituent components. This work investigates the use of ICA for automatically extracting features from in vivo MR spectra. After its limits are assessed on artificial data, the method is applied to a set of brain tumor spectra. ICA automatically, and in an unsupervised fashion, decomposes the signals into interpretable components. Moreover, the spectral decomposition achieved by the ICA leads to the separation of some tissue types, which confirms the biochemical relevance of the components.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0740-3194
pubmed:author
pubmed:copyrightInfo
Copyright 2003 Wiley-Liss, Inc.
pubmed:issnType
Print
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
697-703
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2003
pubmed:articleTitle
Independent component analysis for automated decomposition of in vivo magnetic resonance spectra.
pubmed:affiliation
CR-UK Biomedical Magnetic Resonance Research Group, Basic Medical Sciences Department, London, UK. cladroue@sghms.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't