Single particle raster image analysis of diffusion for particle mixtures
Artikel i vetenskaplig tidskrift, 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.



Fluorescent beads

Single particle tracking

Confocal laser scanning microscopy

Particle mixtures

Maximum likelihood


Marco Longfils

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Magnus Röding

RISE Bioscience and Materials

Annika Altskär

RISE Bioscience and Materials

E. Schuster

RISE Bioscience and Materials

Niklas Lorén

RISE Bioscience and Materials

Aila Särkkä

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Mats Rudemo

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Journal of Microscopy

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

Vol. 269 3 269-281


Sannolikhetsteori och statistik

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling