Open AccessEstimating fluorescence lifetimes using the expectation‐maximisation algorithm
Author(s) -
Gao Kai,
Li David DayUei
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.3165
Subject(s) - monte carlo method , algorithm , probabilistic logic , computer science , estimation theory , expectation–maximization algorithm , fluorescence , artificial intelligence , mathematics , maximum likelihood , statistics , physics , optics
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.