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Automatic particle detection in microscopy using temporal correlations
Author(s) -
Röding Magnus,
Deschout Hendrik,
Martens Thomas,
Notelaers Kristof,
Hofkens Johan,
Ameloot Marcel,
Braeckmans Kevin,
Särkkä Aila,
Rudemo Mats
Publication year - 2013
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.22260
Subject(s) - false positive paradox , computer science , set (abstract data type) , particle (ecology) , markov chain monte carlo , particle filter , data set , tracking (education) , noise (video) , reproducibility , single particle analysis , artificial intelligence , pattern recognition (psychology) , statistics , mathematics , bayesian probability , physics , image (mathematics) , kalman filter , psychology , pedagogy , oceanography , programming language , geology , aerosol , meteorology
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. Microsc. Res. Tech., 76:997–1006, 2013 . © 2013 Wiley Periodicals, Inc.