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June 2010 Likelihood inference for particle location in fluorescence microscopy
John Hughes, John Fricks, William Hancock
Ann. Appl. Stat. 4(2): 830-848 (June 2010). DOI: 10.1214/09-AOAS299


We introduce a procedure to automatically count and locate the fluorescent particles in a microscopy image. Our procedure employs an approximate likelihood estimator derived from a Poisson random field model for photon emission. Estimates of standard errors are generated for each image along with the parameter estimates, and the number of particles in the image is determined using an information criterion and likelihood ratio tests. Realistic simulations show that our procedure is robust and that it leads to accurate estimates, both of parameters and of standard errors. This approach improves on previous ad hoc least squares procedures by giving a more explicit stochastic model for certain fluorescence images and by employing a consistent framework for analysis.


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John Hughes. John Fricks. William Hancock. "Likelihood inference for particle location in fluorescence microscopy." Ann. Appl. Stat. 4 (2) 830 - 848, June 2010.


Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62108
MathSciNet: MR2758423
Digital Object Identifier: 10.1214/09-AOAS299

Keywords: fluorescence microscopy , Maximum likelihood methods , Molecular motor , nanotechnology , organelle , particle tracking , Poisson random field

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.4 • No. 2 • June 2010
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