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Verfasst von:Jaiswal, Astha [VerfasserIn]   i
 Godinez, William J. [VerfasserIn]   i
 Eils, Roland [VerfasserIn]   i
 Lehmann, Maik J. [VerfasserIn]   i
 Rohr, Karl [VerfasserIn]   i
Titel:Tracking virus particles in fluorescence microscopy images using multi-scale detection and multi-frame association
Verf.angabe:Astha Jaiswal, Member IEEE, William J. Godinez, Member IEEE, Roland Eils, Maik Jörg Lehmann, and Karl Rohr
E-Jahr:2015
Jahr:17 July 2015
Umfang:15 S.
Fussnoten:Gesehen am 23.06.2020
Titel Quelle:Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on image processing
Ort Quelle:New York, NY : IEEE, 1992
Jahr Quelle:2015
Band/Heft Quelle:24(2015), 11, Seite 4122-4136
ISSN Quelle:1941-0042
Abstract:Automatic fluorescent particle tracking is an essential task to study the dynamics of a large number of biological structures at a sub-cellular level. We have developed a probabilistic particle tracking approach based on multi-scale detection and two-step multi-frame association. The multi-scale detection scheme allows coping with particles in close proximity. For finding associations, we have developed a two-step multi-frame algorithm, which is based on a temporally semiglobal formulation as well as spatially local and global optimization. In the first step, reliable associations are determined for each particle individually in local neighborhoods. In the second step, the global spatial information over multiple frames is exploited jointly to determine optimal associations. The multi-scale detection scheme and the multi-frame association finding algorithm have been combined with a probabilistic tracking approach based on the Kalman filter. We have successfully applied our probabilistic tracking approach to synthetic as well as real microscopy image sequences of virus particles and quantified the performance. We found that the proposed approach outperforms previous approaches.
DOI:doi:10.1109/TIP.2015.2458174
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1109/TIP.2015.2458174
 DOI: https://doi.org/10.1109/TIP.2015.2458174
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Algorithms
 automatic fluorescent particle tracking
 biological structures
 Biology
 biology computing
 cellular biophysics
 fluorescence
 fluorescence microscopy image sequence
 global spatial information
 Image Processing, Computer-Assisted
 image sequences
 Kalman filter
 Kalman filters
 Microscopy
 Microscopy, Fluorescence
 multi-frame association
 multi-scale particle detection
 multiscale detection
 Noise
 optimisation
 optimization
 Optimization
 Particle tracking
 Probabilistic logic
 probabilistic particle tracking approach
 probability
 Signal-To-Noise Ratio
 sub-cellular level
 tracking algorithms
 Trajectory
 two-step multiframe association finding algorithm
 Virion
 Virology
 virus particle tracking
 Virus particle tracking
K10plus-PPN:1700118757
Verknüpfungen:→ Zeitschrift

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