Abstract
Several major advances in Cell and Molecular Biology have been made possible by recent advances in livecell microscopy imaging. To support these efforts, automated image analysis methods such as cell segmentation and tracking during a time-series analysis are needed. To this aim, one important step is the validation of such image processing methods. Ideally, the "ground truth" should be known, which is possible only by manually labelling images or in artificially produced images. To simulate artificial images, we have developed a platform for simulating biologically inspired objects, which generates bodies with various morphologies and kinetics and, that can aggregate to form clusters. Using this platform, we tested and compared four tracking algorithms: Simple Nearest-Neighbour (NN), NN with Morphology and two DBSCAN-based methods. We show that Simple NN works well for small object velocities, while the others perform better on higher velocities and when clustering occurs. Our new platform for generating new benchmark images to test image analysis algorithms is openly available at (http://griduni.uninova.pt/Clustergen/ClusterGen-v1.0.zip).
Original language | English |
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Title of host publication | Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications |
Publisher | SciTePress - Science and Technology Publications |
Pages | 44-55 |
Number of pages | 12 |
ISBN (Print) | 978-989-758-199-1 |
DOIs | |
Publication status | Published - 2016 |
Event | 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Lisbon, Portugal Duration: 29 Jul 2016 → 31 Jul 2016 |
Conference
Conference | 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications |
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Abbreviated title | SIMULTECH 2016 |
Country/Territory | Portugal |
City | Lisbon |
Period | 29/07/16 → 31/07/16 |
Keywords
- cell tracking
- Cluster Tracking
- Microscopy
- Synthetic Time-lapse Image Simulation