ArticleBiophysical journal2010
Analysis of video-based microscopic particle trajectories using Kalman filtering.
Article in Biophysical journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Kalman-like Self-Tuned Sensitivity in Biophysical Sensing.Cell systems · 2019Article
- Enhanced Diffusion of Catalytically Active Enzymes.ACS central science · 2019Review
- Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis.Chemical reviews · 2017Article
- Depicting Binding-Mediated Translocation of HIV-1 Tat Peptides in Living Cells with Nanoscale Pens of Tat-Conjugated Quantum Dots.Sensors (Basel, Switzerland) · 2017Article
- Review
- Interactive, computer-assisted tracking of speckle trajectories in fluorescence microscopy: application to actin polymerization and membrane fusion.Biophysical journal · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes.
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Registered trials
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