Tracking virus particles in fluorescence microscopy images using multi-scale detection and multi-frame association
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 det...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
17 July 2015
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| In: |
IEEE transactions on image processing
Year: 2015, Jahrgang: 24, Heft: 11, Pages: 4122-4136 |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2015.2458174 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1109/TIP.2015.2458174
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| Verfasserangaben: | Astha Jaiswal, Member IEEE, William J. Godinez, Member IEEE, Roland Eils, Maik Jörg Lehmann, and Karl Rohr |
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| 520 | |a 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. | ||
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a automatic fluorescent particle tracking | |
| 650 | 4 | |a biological structures | |
| 650 | 4 | |a Biology | |
| 650 | 4 | |a biology computing | |
| 650 | 4 | |a cellular biophysics | |
| 650 | 4 | |a fluorescence | |
| 650 | 4 | |a fluorescence microscopy image sequence | |
| 650 | 4 | |a global spatial information | |
| 650 | 4 | |a Image Processing, Computer-Assisted | |
| 650 | 4 | |a image sequences | |
| 650 | 4 | |a Kalman filter | |
| 650 | 4 | |a Kalman filters | |
| 650 | 4 | |a Microscopy | |
| 650 | 4 | |a Microscopy, Fluorescence | |
| 650 | 4 | |a multi-frame association | |
| 650 | 4 | |a multi-scale particle detection | |
| 650 | 4 | |a multiscale detection | |
| 650 | 4 | |a Noise | |
| 650 | 4 | |a optimisation | |
| 650 | 4 | |a optimization | |
| 650 | 4 | |a Optimization | |
| 650 | 4 | |a Particle tracking | |
| 650 | 4 | |a Probabilistic logic | |
| 650 | 4 | |a probabilistic particle tracking approach | |
| 650 | 4 | |a probability | |
| 650 | 4 | |a Signal-To-Noise Ratio | |
| 650 | 4 | |a sub-cellular level | |
| 650 | 4 | |a tracking algorithms | |
| 650 | 4 | |a Trajectory | |
| 650 | 4 | |a two-step multiframe association finding algorithm | |
| 650 | 4 | |a Virion | |
| 650 | 4 | |a Virology | |
| 650 | 4 | |a virus particle tracking | |
| 650 | 4 | |a Virus particle tracking | |
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