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Journal of Biomedical Optics • Open Access

Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging
Author(s): Antonello E. Spinelli; Federico Boschi

Paper 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 32P-ATP and 18F-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.

Paper Details

Date Published: 1 December 2011
PDF: 4 pages
J. Biomed. Opt. 16(12) 120507 doi: 10.1117/1.3663442
Published in: Journal of Biomedical Optics Volume 16, Issue 12
Show Author Affiliations
Antonello E. Spinelli, Fondazione San Raffaele del Monte Tabor (Italy)
Federico Boschi, Univ. degli Studi di Verona (Italy)


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