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. 2009 Dec;2009(12):pdb.top65.
doi: 10.1101/pdb.top65.

Computational image analysis of cellular dynamics: a case study based on particle tracking

Khuloud Jaqaman, Gaudenz Danuser

Computational image analysis of cellular dynamics: a case study based on particle tracking

Khuloud Jaqaman et al. Cold Spring Harb Protoc. 2009 Dec.
No abstract available

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Figures

Figure 1
Figure 1
PT builds on essential steps (detection and trajectory construction) and optional but recommended steps (motion modeling and trajectory diagnosis). Image acquisition and analysis are tightly coupled.
Figure 2
Figure 2
Trajectory construction via local nearest neighbor (LNN) assignment. (A) LNN succeeds when ρ = (average frame-to-frame displacement)/( average nearest neighbor distance) ≪ 0.5. (B) LNN fails when ρ = > ~0.2.
Figure 3
Figure 3
Performance triangle of an experimental setup, as determined by the specimen, microscope hardware, and image analysis software. Spatiotemporal sampling, SNR and observation length are interdependent and conflicting imaging parameters. Modified, with permission, from (Dorn et al. 2008).
Figure 4
Figure 4
Data analysis to ensure proper temporal sampling of the measured dynamics. (A) Effect of sampling rate on diffusion coefficient estimation (line fits) for confined diffusive motion. (B) Effect of sampling rate on speed estimation for periodic and quasi-periodic movement.
Figure 5
Figure 5
Trajectory diagnosis for evaluation of tracking. (A) Cut-off histogram of frame-to-frame linking distances, a sign that the search radius is too small. (B) Slowly decaying histogram of frame-to-frame linking distances, indicating a sufficiently large search radius. (C) Sampling rate-independent distribution of gap lengths, expressed in frames. (D) Histogram of gap lengths with a too large gap closing time window (gray) and an appropriate time window (black).
See this image and copyright information in PMC

References

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