NDLI: Estimating fluorescence lifetimes using the expectation-maximisation algorithm

Content Provider	IET Digital Library
Author	Gao, Kai Li, David Day Uei
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.
Starting Page	14
Ending Page	16
Page Count	3
ISSN	00135194
Volume Number	54
e-ISSN	1350911X
Issue Number	Issue 1, Jan (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/54/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2017.3165
Journal	Electronics Letters
Publisher Date	2017-11-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Measurement And Imaging Computer Vision And Image Processing Technique EM Algorithm EM Technique Expectation-maximisation Algorithm Fluorescence Fluorescence Lifetime Imaging Experiments Interpolation And Function Approximation Machine Learning Medical Image Processing Monte Carlo Method Monte Carlo Simulation Method Numerical Analysis Numerical Approximation And Analysis Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Probabilistic Model Parameter Estimation Probability Probability Theory Statistics Stochastic Linearised SCUC Time-correlated Single-photon Counting
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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