Automatic particle detection in microscopy using temporal correlations. Issue 10 (16th July 2013)
- Record Type:
- Journal Article
- Title:
- Automatic particle detection in microscopy using temporal correlations. Issue 10 (16th July 2013)
- Main Title:
- Automatic particle detection in microscopy using temporal correlations
- Authors:
- Röding, Magnus
Deschout, Hendrik
Martens, Thomas
Notelaers, Kristof
Hofkens, Johan
Ameloot, Marcel
Braeckmans, Kevin
Särkkä, Aila
Rudemo, Mats - Abstract:
- <abstract abstract-type="main"> <title>ABSTRACT</title> <p>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. <italic>Microsc. Res. Tech., 76:997–1006, 2013</italic>. © 2013 Wiley Periodicals, Inc.</p><abstract abstract-type="main"> <title>ABSTRACT</title> <p>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. <italic>Microsc. Res. Tech., 76:997–1006, 2013</italic>. © 2013 Wiley Periodicals, Inc.</p> </abstract> … (more)
- Is Part Of:
- Microscopy research and technique. Volume 76:Issue 10(2013:Oct.)
- Journal:
- Microscopy research and technique
- Issue:
- Volume 76:Issue 10(2013:Oct.)
- Issue Display:
- Volume 76, Issue 10 (2013)
- Year:
- 2013
- Volume:
- 76
- Issue:
- 10
- Issue Sort Value:
- 2013-0076-0010-0000
- Page Start:
- 997
- Page End:
- 1006
- Publication Date:
- 2013-07-16
- Subjects:
- Electron microscopy -- Technique -- Periodicals
Microscopy -- Periodicals
Microscopy -- Technique -- Periodicals
502.825 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0029 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/jemt.22260 ↗
- Languages:
- English
- ISSNs:
- 1059-910X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5760.600850
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 4025.xml