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

Krog, Jens LU ; Dvirnas, Albertas LU ; Ström, Oskar E LU orcid ; Beech, Jason P LU ; Tegenfeldt, Jonas O LU orcid ; Müller, Vilhelm ; Westerlund, Fredrik and Ambjörnsson, Tobias LU (2024) In PLoS ONE 19(4). p.0300122-0300122
Abstract

We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the... (More)

We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
PLoS ONE
volume
19
issue
4
pages
0300122 - 0300122
publisher
Public Library of Science (PLoS)
external identifiers
  • scopus:85189683937
  • pmid:38578724
ISSN
1932-6203
DOI
10.1371/journal.pone.0300122
language
English
LU publication?
yes
additional info
Copyright: © 2024 Krog et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
id
35d95d11-f269-4d7b-9f14-0fce0d0dcb66
date added to LUP
2024-04-07 14:32:35
date last changed
2024-06-18 04:01:23
@article{35d95d11-f269-4d7b-9f14-0fce0d0dcb66,
  abstract     = {{<p>We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.</p>}},
  author       = {{Krog, Jens and Dvirnas, Albertas and Ström, Oskar E and Beech, Jason P and Tegenfeldt, Jonas O and Müller, Vilhelm and Westerlund, Fredrik and Ambjörnsson, Tobias}},
  issn         = {{1932-6203}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{4}},
  pages        = {{0300122--0300122}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Photophysical image analysis : Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0300122}},
  doi          = {{10.1371/journal.pone.0300122}},
  volume       = {{19}},
  year         = {{2024}},
}