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

التفاصيل البيبلوغرافية
العنوان: Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices.
المؤلفون: Krog J; Centre for Environmental and Climate Science, Lund University, Lund, Sweden., Dvirnas A; Centre for Environmental and Climate Science, Lund University, Lund, Sweden., Ström OE; Department of Physics and NanoLund, Lund University, Lund, Sweden., Beech JP; Department of Physics and NanoLund, Lund University, Lund, Sweden., Tegenfeldt JO; Department of Physics and NanoLund, Lund University, Lund, Sweden., Müller V; Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden., Westerlund F; Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden., Ambjörnsson T; Centre for Environmental and Climate Science, Lund University, Lund, Sweden.
المصدر: PloS one [PLoS One] 2024 Apr 05; Vol. 19 (4), pp. e0300122. Date of Electronic Publication: 2024 Apr 05 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Electrons* , Diagnostic Imaging*, Image Processing, Computer-Assisted/methods ; Fourier Analysis
مستخلص: 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.
Competing Interests: The authors have declared that no competing interests exist.
(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.)
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تواريخ الأحداث: Date Created: 20240405 Date Completed: 20240408 Latest Revision: 20240408
رمز التحديث: 20240408
مُعرف محوري في PubMed: PMC10997106
DOI: 10.1371/journal.pone.0300122
PMID: 38578724
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0300122