Abstract:
:Clustering analysis (CA) and principal component analysis (PCA) were applied to dynamic Cerenkov luminescence images (dCLI). In order to investigate the performances of the proposed approaches, two distinct dynamic data sets obtained by injecting mice with (32)P-ATP and (18)F-FDG were acquired using the IVIS 200 optical imager. The k-means clustering algorithm has been applied to dCLI and was implemented using interactive data language 8.1. We show that cluster analysis allows us to obtain good agreement between the clustered and the corresponding emission regions like the bladder, the liver, and the tumor. We also show a good correspondence between the time activity curves of the different regions obtained by using CA and manual region of interest analysis on dCLIT and PCA images. We conclude that CA provides an automatic unsupervised method for the analysis of preclinical dynamic Cerenkov luminescence image data.
journal_name
J Biomed Optjournal_title
Journal of biomedical opticsauthors
Spinelli AE,Boschi Fdoi
10.1117/1.3663442subject
Has Abstractpub_date
2011-12-01 00:00:00pages
120506issue
12eissn
1083-3668issn
1560-2281journal_volume
16pub_type
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journal_title:Journal of biomedical optics
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journal_title:Journal of biomedical optics
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