Histogram clustering for rapid time-domain fluorescence lifetime image analysis

We propose a histogram classification (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 us per histograms on MATLAB R2016a, 64-bit with the Intel(R) Celeron(R) CPU (2950M @ 2GHz).