2006
DOI: 10.1016/j.mimet.2006.05.019
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A novel fluorescence imaging technique combining deconvolution microscopy and spectral analysis for quantitative detection of opportunistic pathogens

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Cited by 11 publications
(10 citation statements)
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“…Major problems we encountered during quantitation from the small glass slide chip included variability in fluorescence signals, interference from nearby neighbors, and out-of-focus blur. 16,18,21 To overcome them, we applied 2-D restorative deconvolution (i.e., Lucy-Richardson algorithm 22 ) during postimage processing, which could reassign out-of-focus blur mathematically to its point of origin and therefore permit counting of the absolute number of cells on the same ROI.…”
Section: Discussionmentioning
confidence: 99%
“…Major problems we encountered during quantitation from the small glass slide chip included variability in fluorescence signals, interference from nearby neighbors, and out-of-focus blur. 16,18,21 To overcome them, we applied 2-D restorative deconvolution (i.e., Lucy-Richardson algorithm 22 ) during postimage processing, which could reassign out-of-focus blur mathematically to its point of origin and therefore permit counting of the absolute number of cells on the same ROI.…”
Section: Discussionmentioning
confidence: 99%
“…Although the problems associated with autofluorescence were less severe in our seawater sample, compared to those in coral tissues, the removal of spectra originating from sources other than fluorescent probes (e.g., fluorescent eukaryotic and bacterial cells) significantly facilitated the identification of target organisms. Second, the technology not only enabled the separation of probe-conferred and other interfering spectra, but also the separation of multiple different probe-conferred spectra assigned to target organisms Le Puil et al 2006).…”
Section: Improving Accuracy Of Target Detection In Fish Using Spectramentioning
confidence: 99%
“…The imaging platform allowed acquisition of specific spectral signatures for each pixel of the image and, therefore, allowed separation of the microsphere fluorescent signal from the inherent noise signal of the sample material. Based on previous works (Chang et al, 2003;Biggerstaff et al, 2006;Le Puil et al, 2006), a quantitation protocol was derived by way of AutoDeblur ® (AutoQuant Imaging Inc., Watervliet, NY). Fluorescence areas were quantified for each image to obtain the density of microspheres in each field of view.…”
Section: Microsphere Tracersmentioning
confidence: 99%