PT  - JOURNAL ARTICLE
AU  - Mohamadreza Fazel
AU  - Michael J. Wester
AU  - Hanieh Mazloom-Farsibaf
AU  - Marjolein B. M. Meddens
AU  - Alexandra Eklund
AU  - Thomas Schlichthaerle
AU  - Florian Schueder
AU  - Ralf Jungmann
AU  - Keith A. Lidke
TI  - Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo
AID  - 10.1101/530261
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 530261
4099  - http://biorxiv.org/content/early/2019/01/26/530261.short
4100  - http://biorxiv.org/content/early/2019/01/26/530261.full
AB  - 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.