Likelihood inference for particle location in fluorescence microscopy

TL;DR

This paper presents a statistically rigorous method for automatically counting and locating fluorescent particles in microscopy images using a likelihood-based approach derived from a Poisson model, providing accurate estimates and standard errors.

Contribution

It introduces a novel likelihood inference procedure that improves accuracy and robustness over previous ad hoc methods for particle localization in fluorescence microscopy images.

Findings

The method provides accurate parameter estimates.

Estimates of standard errors are reliably generated.

The approach is robust in realistic simulations.

Abstract

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.