Likelihood inference for particle location in fluorescence microscopy

John Hughes, John Fricks, William Hancock

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)830-848
Number of pages19
JournalAnnals of Applied Statistics
Issue number2
StatePublished - Jun 1 2010


  • Fluorescence microscopy
  • Maximum likelihood methods
  • Molecular motor
  • Nanotechnology
  • Organelle
  • Particle tracking
  • Poisson random field


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