Estimating fluorescence lifetimes using the expectation-maximisation algorithm

Research output: Contribution to journalArticle

Abstract

The expectation-maximisation (EM) algorithm uses incomplete data to get the estimation of the probabilistic model parameter, and it has been widely used in machine learning. EM techniques are applied to estimate fluorescence lifetimes in time-correlated single-photon counting based fluorescence lifetime imaging experiments without measuring the instrument response functions. The results of Monte Carlo simulations indicate that the proposed approach can obtain better or comparable accuracy and precision performances than the previously reported method.
LanguageEnglish
Pages1-2
Number of pages2
JournalElectronics Letters
Volume54
Issue number1
DOIs
StatePublished - 11 Jan 2018

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Fluorescence
Learning systems
Photons
Imaging techniques
Experiments
Statistical Models
Monte Carlo simulation

Keywords

  • fluorescence
  • expectation-maximization algorithm
  • Monte Carlo simulations

Cite this

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title = "Estimating fluorescence lifetimes using the expectation-maximisation algorithm",
abstract = "The expectation-maximisation (EM) algorithm uses incomplete data to get the estimation of the probabilistic model parameter, and it has been widely used in machine learning. EM techniques are applied to estimate fluorescence lifetimes in time-correlated single-photon counting based fluorescence lifetime imaging experiments without measuring the instrument response functions. The results of Monte Carlo simulations indicate that the proposed approach can obtain better or comparable accuracy and precision performances than the previously reported method.",
keywords = "fluorescence, expectation-maximization algorithm, Monte Carlo simulations",
author = "Kai Gao and Li, {David Day-Uei}",
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Estimating fluorescence lifetimes using the expectation-maximisation algorithm. / Gao, Kai; Li, David Day-Uei.

In: Electronics Letters, Vol. 54, No. 1, 11.01.2018, p. 1-2.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Estimating fluorescence lifetimes using the expectation-maximisation algorithm

AU - Gao,Kai

AU - Li,David Day-Uei

N1 - This paper is a postprint of a paper submitted to and accepted for publication in Electronics Letters and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.

PY - 2018/1/11

Y1 - 2018/1/11

N2 - The expectation-maximisation (EM) algorithm uses incomplete data to get the estimation of the probabilistic model parameter, and it has been widely used in machine learning. EM techniques are applied to estimate fluorescence lifetimes in time-correlated single-photon counting based fluorescence lifetime imaging experiments without measuring the instrument response functions. The results of Monte Carlo simulations indicate that the proposed approach can obtain better or comparable accuracy and precision performances than the previously reported method.

AB - The expectation-maximisation (EM) algorithm uses incomplete data to get the estimation of the probabilistic model parameter, and it has been widely used in machine learning. EM techniques are applied to estimate fluorescence lifetimes in time-correlated single-photon counting based fluorescence lifetime imaging experiments without measuring the instrument response functions. The results of Monte Carlo simulations indicate that the proposed approach can obtain better or comparable accuracy and precision performances than the previously reported method.

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KW - Monte Carlo simulations

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