Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia

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DOIResolve DOI: http://doi.org/10.1016/S0895-6111(97)00018-9
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TypeArticle
Journal titleComputerized Medical Imaging and Graphics
ISSN0895-6111
Volume21
Issue5
Pages299308; # of pages: 10
SubjectNear-infrared imaging; Forearm ischemia; Image analysis; PCA; Fuzzy C-means clustering
AbstractFuzzy C-means clustering and principal components analysis were used to analyze a temporal series of near-IR images taken of a human forearm during periods of venous outflow restriction and complete forearm ischemia. The principal component eigen-time course analysis provided no useful information and the principal component eigen-image analysis gave results that correlated poorly with anatomical features. The fuzzy C-means clustering analysis, on the other hand, showed distinct regional differences in the hemodynamic response and scattering properties of the tissue, which correlated well with the anatomical features of the forearm.
Publication date
PublisherElsevier B.V.
LanguageEnglish
AffiliationNRC Institute for Biodiagnostics; National Research Council Canada
Peer reviewedYes
NRC number461
NPARC number9148331
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Record identifieraf3fa8bd-3881-42cd-84fa-717be0cddc87
Record created2009-06-25
Record modified2016-10-31
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