Point-spread function retrieval for fluorescence microscopy

International audienceIn this paper we propose a method for retrieving the Point-Spread Function (PSF) of an imaging system given the observed images of fluoroscentmicrospheres. Theoretically calculated PSFs often lack the experimental or microscope specific signatureswhile empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to amodel based on the scalar diffraction theory. The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data

Particle Filtering Methods for Subcellular Motion Analysis

Advances in fluorescent probing and microscopic imaging technology have revolutionized biology in the past decade and have opened the door for studying subcellular dynamical processes. However, accurate and reproducible methods for processing and analyzing the images acquired for such studies are still lacking. Since manual image analysis is time consuming, potentially inaccurate, and poorly reproducible, many biologically highly relevant questions are either left unaddressed, or are answered with great uncertainty. The subject of this thesis is particle filtering methods and their application for multiple object tracking in different biological imaging applications. Particle filtering is a technique for implementing recursive Bayesian filtering by Monte Carlo sampling. A fundamental concept behind the Bayesian approach for performing inference is the possibility to encode the information about the imaging system, possible noise sources, and the system dynamics in terms of probability density functions. In this thesis, a set of novel PF based metho