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Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying

Jens Krog1, Albertas Dvirnas1, Oskar E Ström2

  • 1Centre for Environmental and Climate Science, Lund University, Lund, Sweden.

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|April 5, 2024
PubMed
Summary
This summary is machine-generated.

We developed photophysical image analysis (PIA) for unsupervised image thresholding using electron-multiplying charge-coupled device (EMCCD) cameras. This method provides a priori misclassification rates for improved accuracy in image analysis.

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Area of Science:

  • Image analysis
  • Computational imaging
  • Photonics

Background:

  • Electron-multiplying charge-coupled device (EMCCD) cameras are widely used for low-light imaging.
  • Existing image thresholding methods often lack a priori error estimation.
  • Accurate noise modeling is crucial for reliable image analysis.

Purpose of the Study:

  • To introduce a novel unsupervised probabilistic image thresholding pipeline for EMCCD cameras.
  • To develop a method for a priori determination of misclassified pixels.
  • To provide a robust framework for photophysical image analysis (PIA).

Main Methods:

  • Developed a closed-form analytic expression for the characteristic function of EMCCD image counts.
  • Incorporated photon arrival stochasticity and camera detection noise.
  • Estimated background photon statistics (λbg) using a truncated fit procedure.
  • Introduced a probabilistic thresholding method with a priori error estimation.
  • Validated using synthetic images and compared against the Otsu method.

Main Results:

  • The proposed method accurately estimates background photon statistics and camera noise parameters.
  • Probabilistic thresholding allows for a priori determination of misclassification fractions.
  • Demonstrated superior performance compared to the Otsu method in synthetic image benchmarks.
  • Successfully applied to segment synthetic and experimental images.

Conclusions:

  • The developed PIA pipeline offers automated, unsupervised, and probabilistic image thresholding for EMCCD cameras.
  • This approach enables precise error quantification in image analysis.
  • Publicly available software facilitates advanced photophysical image analysis.