1 December 2011 Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging
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J. of Biomedical Optics, 16(12), 120507 (2011). doi:10.1117/1.3663442
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.
Antonello E. Spinelli, Federico Boschi, "Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging," Journal of Biomedical Optics 16(12), 120507 (1 December 2011). http://dx.doi.org/10.1117/1.3663442
Submission: Received ; Accepted
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KEYWORDS
Principal component analysis

Tumors

Luminescence

Liver

Bladder

Image segmentation

Data acquisition

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