Automatic Particle Detection in Microscopy Using Temporal Correlations
Journal article, 2013

One of the fundamental problems in the analysis of single particle tracking data is the detection of individual particle positions from microscopy images. Distinguishing true particles from noise with a minimum of false positives and false negatives is an important step that will have substantial impact on all further analysis of the data. A common approach is to obtain a plausible set of particles from a larger set of candidate particles by filtering using manually selected threshold values for intensity, size, shape, and other parameters describing a particle. This introduces subjectivity into the analysis and hinders reproducibility. In this paper, we introduce a method for automatic selection of these threshold values based on maximizing temporal correlations in particle count time series. We use Markov Chain Monte Carlo to find the threshold values corresponding to the maximum correlation, and we study several experimental data sets to assess the performance of the method in practice by comparing manually selected threshold values from several independent experts with automatically selected threshold values. We conclude that the method produces useful results, reducing subjectivity and the need for manual intervention, a great benefit being its easy integratability into many already existing particle detection algorithms.

image analysis

unsupervised learning

optical microscopy

fluorescence microscopy

Author

Magnus Röding

University of Gothenburg

SuMo Biomaterials

Chalmers, Mathematical Sciences, Mathematical Statistics

Hendrik Deschout

Ghent university

Thomas Martens

Ghent university

Kristof Notelaers

KU Leuven

Universiteit Hasselt

Johan Hofkens

KU Leuven

M. Ameloot

Universiteit Hasselt

Kevin Braeckmans

Ghent university

Aila Särkkä

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Mats Rudemo

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Microscopy Research and Technique

1059-910X (ISSN)

Vol. 76 10 997-1006

Subject Categories

Biological Sciences

DOI

10.1002/jemt.22260

More information

Latest update

5/29/2018