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Automatic Particle Detection in Microscopy Using Temporal Correlations

Magnus Röding (Institutionen för matematiska vetenskaper, matematisk statistik ; SuMo Biomaterials) ; Hendrik Deschout ; Thomas Martens ; Kristof Notelaers ; Johan Hofkens ; Marcel Ameloot ; Kevin Braeckmans ; Aila Särkkä (Institutionen för matematiska vetenskaper, matematisk statistik) ; Mats Rudemo (Institutionen för matematiska vetenskaper, matematisk statistik)
Microscopy Research and Technique (1059-910X). Vol. 76 (2013), 10, p. 997-1006.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: optical microscopy, fluorescence microscopy, image analysis, unsupervised learning

Denna post skapades 2013-11-11. Senast ändrad 2017-01-27.
CPL Pubid: 186377


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)
SuMo Biomaterials


Biologiska vetenskaper

Chalmers infrastruktur