Single particle raster image analysis of diffusion for particle mixtures
Journal article, 2018

Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function.

Single particle tracking

Confocal laser scanning microscopy

Particle mixtures

Maximum likelihood

Fluorescent beads

Diffusion

Bootstrap

Author

Marco Longfils

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Magnus Röding

RISE Research Institutes of Sweden

Annika Altskär

RISE Research Institutes of Sweden

E. Schuster

RISE Research Institutes of Sweden

Niklas Lorén

RISE Research Institutes of Sweden

Aila Särkkä

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Mats Rudemo

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Journal of Microscopy

0022-2720 (ISSN) 1365-2818 (eISSN)

Vol. 269 3 269-281

Subject Categories

Probability Theory and Statistics

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1111/jmi.12625

More information

Latest update

9/19/2018