Histogram clustering for rapid time-domain fluorescence lifetime image analysis

Abstract

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).

A method for the fast and photon‐efficient analysis of time‐domain fluorescence lifetime image data over large dynamic ranges

“…Generally, the CMM algorithm is also compu- tationally efficient and faster than state-of-the-art LSM implementations and has been implemented 'on-chip' with FLIM instrumentation. 19,20,28 The disadvantage of the CMM approach is that multiexponential decays cannot be distinguished and CMM will provide an estimate of an average lifetime instead. If this is desired, a global analysis and multi-exponential fitting with LSM are powerful alternatives.…”

“…The photon efficiency (indicated by the $$F^{\prime }$$‐value) is higher than for LSM for lifetime ranges typically encountered in biological fluorescence experiments, improving precision and accuracy of the lifetime determination for the same photon budget. Generally, the CMM algorithm is also computationally efficient and faster than state‐of‐the‐art LSM implementations and has been implemented ‘on‐chip’ with FLIM instrumentation 19,20,28 …”

Lifetime Determination Algorithms for Time-Domain Fluorescence Lifetime Imaging: A Review

“…The HC method devised by Li et al can improve FLIM analysis speed and accuracy by sorting histograms with similar profiles in a dataset into several clusters and significantly reducing the number of histograms to be analyzed [94]. HC implements clustering with two features of a histogram.…”

“…It is a preprocessing method that can work with the LDAs mentioned above. The performances for producing decay parameter images without and with HC using synthetic and experimental datasets were investigated [94]. The execution time t exe and the mean squared error (MSE) of a FLIM dataset following a bi-exponential decay model with 150 Â 150 pixels and 256 timebins are shown in Table 1.…”

Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine

“…Direction-of-arrivals estimation [ 23 ] was adopted to deliver a non-iterative and model-free lifetime reconstruction strategy, requiring a few time bins. A histogram cluster method [ 24 ] divides histograms into clusters instead of processing histograms pixel-by-pixel, enhancing the analysis speed. However, challenges remain.…”