Name
Abstract | ||
|---|---|---|
Fluorescence time-lapse microscopy is a powerful technique for observing the spatial-temporal behavior of viruses. To quantitatively analyze the exhibited dynamical relationships, tracking of viruses over time is required. We developed probabilistic approaches based on parti- cle filters for tracking multiple virus particles in time-lapse fluorescence microscopy images. We employed a mixture of particle filters as well as independent particle filters. For the latter, we have developed a penal- ization strategy to maintain the identity of the tracked objects in cases where objects are in close proximity. We have applied the approaches to synthetic images and quantified their performance. We have also success- fully applied the approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth from manual tracking. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-78640-5_90 | Bildverarbeitung für die Medizin |
Keywords | Field | DocType |
ground truth,particle filter,fluorescence microscopy | Computer vision,Fluorescence microscope,Particle filter,Fluorescence,Ground truth,Artificial intelligence,Probabilistic logic,Microscopy,Physics | Conference |
Citations | PageRank | References |
2 | 0.44 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| William J. Godinez | 1 | 91 | 10.98 |
| Marko Lampe | 2 | 46 | 4.16 |
| Stefan Wörz | 3 | 256 | 32.58 |
| Barbara Müller | 4 | 17 | 3.02 |
| Roland Eils | 5 | 644 | 70.09 |
| Karl Rohr | 6 | 340 | 48.69 |