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Updated: Oct 22, 2025

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Histogram clustering for rapid time-domain fluorescence lifetime image analysis.

Yahui Li1,2,3, Natakorn Sapermsap4, Jun Yu5

  • 1Key Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi'an Institute of Optics and Precision Mechanics, Xi'an Shaanxi 710049, China.

Biomedical Optics Express
|August 30, 2021
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Summary

Histogram clustering (HC) significantly accelerates fluorescence lifetime imaging (FLIM) analysis, improving speed up to 106x and enhancing accuracy. This method offers faster FLIM data processing for researchers.

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Area of Science:

  • Biomedical Optics
  • Fluorescence Imaging
  • Data Analysis

Background:

  • Fluorescence Lifetime Imaging (FLIM) is crucial for biological research.
  • Pixel-wise and global fitting methods are standard for FLIM analysis.
  • Current FLIM analysis can be time-consuming.

Purpose of the Study:

  • To introduce a novel Histogram Clustering (HC) method.
  • To accelerate fluorescence lifetime imaging (FLIM) analysis.
  • To improve the accuracy of lifetime estimation in FLIM.

Main Methods:

  • Developed and demonstrated the principle of the Histogram Clustering (HC) method.
  • Integrated HC with traditional FLIM analysis techniques.
  • Validated HC using simulated and experimental FLIM datasets.

Main Results:

  • HC achieved analysis speeds up to 106 times faster than traditional methods.
  • The method demonstrated enhanced accuracy in lifetime estimation.
  • Fast analysis strategies were proposed with execution times under 30 μs per histogram.

Conclusions:

  • Histogram Clustering (HC) is an effective method for accelerating FLIM analysis.
  • HC offers a significant speed-up and accuracy improvement for FLIM data.
  • This technique enables faster and more precise FLIM data interpretation.