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Cited by 257

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images

Open access
Fereidouni, Farzad, Bader, Arjen N., Gerritsen, Hans C.·2012·Optics Express

Abstract

A new global analysis algorithm to analyse (hyper-) spectral images is presented. It is based on the phasor representation that has been demonstrated to be very powerful for the analysis of lifetime imaging data. In spectral phasor analysis the fluorescence spectrum of each pixel in the image is Fourier transformed. Next, the real and imaginary components of the first harmonic of the transform are employed as X and Y coordinates in a scatter (spectral phasor) plot. Importantly, the spectral phasor representation allows for rapid (real time) semi-blind spectral unmixing of up to three components in the image. This is demonstrated on slides with fixed cells containing three fluorescent labels. In addition the method is used to analyse autofluorescence of cells in a fresh grass blade. It is shown that the spectral phasor approach is compatible with spectral imaging data recorded with a low number of spectral channels.

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Crossref · 10.1364/oe.20.012729

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Metadata

Authors
Fereidouni, F., Bader, A. N., & Gerritsen, H. C.
Venue
Optics Express
Published
2012-05-22
Volume
20
Issue
12
Pages
12729
ISSN
1094-4087 (ISSN-L)
OpenAlex
W2133362850
Cited by
257
References
14
FWCI
6.19
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images. https://app.cochat.ai/papers/10.1364/oe.20.012729
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Fereidouni, F., Bader, A. N., & Gerritsen, H. C. (2012). Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images. Optics Express. https://doi.org/10.1364/oe.20.012729

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.