Automatic tracking of particles in fluorescence microscopy images is an important task to quantify the dynamic behavior of subcellular and virus structures. We present a novel iterative approach for tracking multiple particles in microscopy data based on a spatial distance model derived under Brownian motion. Our approach exploits the information that the most likely object position at the next time point is at a certain distance from the current position. Information from all particles in a temporal image sequence are combined and all motion-specific parameters are automatically estimated. Experiments using data of the Particle Tracking Challenge as well as real live cell microscopy data displaying hepatocyte growth factor receptors and virus structures show that our approach outperforms previous methods.
|Title of host publication||Proceedings - International Symposium on Biomedical Imaging|
|Number of pages||4|
|Publication status||Published - 2020|
|Name||Proceedings - International Symposium on Biomedical Imaging|
Bibliographical noteFunding Information:
Acknowledgments: This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Projektnummer 240245660 - SFB 1129 (projects Z4, P11). JYL and RB acknowledge the microscopy support from the Infectious Diseases Imaging Platform at the CIID, Heidelberg. We thank Leonid Kostrykin for fruitful discussions.
© 2020 IEEE.
- Biomedical imaging
- Brownian motion
- chi distribution
- microscopy images
- particle tracking
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging