Open AccessHistogram clustering for rapid time-domain fluorescence lifetime image analysis
Author(s) -
Yahui Li,
Natakorn Sapermsap,
Jun Yu,
Jinshou Tian,
Yu Chen,
David Li
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.427532
Subject(s) - histogram , cluster analysis , computer science , pixel , matlab , fluorescence lifetime imaging microscopy , k means clustering , time domain , fluorescence , artificial intelligence , pattern recognition (psychology) , image (mathematics) , optics , computer vision , physics , operating system
We propose a histogram clustering (HC) method to accelerate fluorescence lifetime imaging (FLIM) analysis in pixel-wise and global fitting modes. The proposed method's principle was demonstrated, and the combinations of HC with traditional FLIM analysis were explained. We assessed HC methods with both simulated and experimental datasets. The results reveal that HC not only increases analysis speed (up to 106 times) but also enhances lifetime estimation accuracy. Fast lifetime analysis strategies were suggested with execution times around or below 30 μ s per histograms on MATLAB R2016a, 64-bit with the Intel Celeron CPU (2950M @ 2GHz).