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Journal Article

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

MPS-Authors
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Eklund,  Alexandra S.
Jungmann, Ralf / Molecular Imaging and Bionanotechnology, Max Planck Institute of Biochemistry, Max Planck Society;

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Schlichthaerle,  Thomas
Jungmann, Ralf / Molecular Imaging and Bionanotechnology, Max Planck Institute of Biochemistry, Max Planck Society;

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Schueder,  Florian
Jungmann, Ralf / Molecular Imaging and Bionanotechnology, Max Planck Institute of Biochemistry, Max Planck Society;

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Jungmann,  Ralf
Jungmann, Ralf / Molecular Imaging and Bionanotechnology, Max Planck Institute of Biochemistry, Max Planck Society;

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Citation

Fazel, M., Wester, M. J., Mazloom-Farsibaf, H., Meddens, M. B. M., Eklund, A. S., Schlichthaerle, T., et al. (2019). Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo. SCIENTIFIC REPORTS, 9: 13791. doi:10.1038/s41598-019-50232-x.


Cite as: https://hdl.handle.net/21.11116/0000-0005-57ED-F
Abstract
In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.