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A stochastic model for electron multiplication charge-coupled devices--from theory to practice
Michael Hirsch1, Richard J Wareham, Marisa L Martin-Fernandez
1Central Laser Facility, Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Harwell Oxford, Didcot, United Kingdom. Michael.Hirsch@stfc.ac.uk
Plos One
|February 6, 2013
Summary
Electron multiplication charge-coupled devices (EMCCDs) are crucial for low-light imaging but have complex noise. This study presents a physical model to accurately analyze EMCCD noise for improved biological imaging.
Area of Science:
- Scientific Imaging
- Optical Physics
- Biophysics
Background:
- Electron multiplication charge-coupled devices (EMCCDs) are vital for low-light detection and biological fluorescence imaging.
- The complex statistical noise properties of EMCCDs are often inadequately addressed in data analysis.
- Accurate noise characterization is essential for advanced analysis techniques like Bayesian and maximum-likelihood methods.
Purpose of the Study:
- To develop and present a detailed physical model for EMCCD noise properties.
- To provide a likelihood function for image pixel counts based on incident light intensity.
- To guide the bioimaging community in improving EMCCD data analysis.
Main Methods:
- Development of a comprehensive physical model for EMCCD noise.
- Formulation of a pixel-wise likelihood function accounting for incident intensity.
- Description of methods for measuring model parameters using calibration images.
Main Results:
- A detailed physical model for EMCCD noise characteristics is presented.
- A likelihood function is derived for quantitative analysis of EMCCD images.
- Practical methods for parameter estimation from calibration data are explained.
Conclusions:
- Understanding EMCCD noise properties is critical for robust quantitative biological imaging.
- The presented physical model and likelihood function facilitate improved EMCCD data analysis.
- This work aims to enhance the utility of EMCCDs in demanding bioimaging applications.
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