Bayesian filtering for spatial estimation of photo-switching fluorophores imaged in Super-resolution fluorescence microscopy

The success of many Super-resolution fluorescence microscopy methods lie in the exploitation of the inherent stochasticity of a light emitting molecule's photon emission state, allowing sparse subsets of molecules to be spatially detected with high precision. This photo-switching behavior, howe...

Full description

Saved in:
Bibliographic Details
Main Authors: Patel, Lekha, Cohen, Edward A.K.
Format: Conference Proceeding
Language:English
Subjects:
Bayes filter
detection probability
fluorescence microscopy
Image resolution
Microscopy
Noise measurement
photo-switching
Photonics
Radio frequency
random set
reversible jump Markov Chain Monte Carlo
spatial-temporal point process
state-space modeling
Super-resolution imaging
target detection
Zinc
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The success of many Super-resolution fluorescence microscopy methods lie in the exploitation of the inherent stochasticity of a light emitting molecule's photon emission state, allowing sparse subsets of molecules to be spatially detected with high precision. This photo-switching behavior, however, induces multiple localizations from each molecule during an imaging experiment, which therefore gives rise to misleading representations of their true spatial locations. By formulating a state-space model relating true molecular positions with observation sets collected across time, we show that the full Bayes filter for this problem can be derived and positions recovered via a Markov Chain Monte Carlo sampler.
ISSN:2576-2303
DOI:10.1109/ACSSC.2018.8645460