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Cited by 1,392

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Feature point tracking and trajectory analysis for video imaging in cell biology

Open access
Sbalzarini, I.F., Koumoutsakos, P.·2005·Journal of Structural Biology

Abstract

This paper presents a computationally efficient, two-dimensional, feature point tracking algorithm for the automated detection and quantitative analysis of particle trajectories as recorded by video imaging in cell biology. The tracking process requires no a priori mathematical modeling of the motion, it is self-initializing, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. The efficiency of the algorithm is validated on synthetic video data where it is compared to existing methods and its accuracy and precision are assessed for a wide range of signal-to-noise ratios. The algorithm is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. Its applicability is demonstrated in three case studies involving transport of low-density lipoproteins in endosomes, motion of fluorescently labeled Adenovirus-2 particles along microtubules, and tracking of quantum dots on the plasma membrane of live cells. The present automated tracking process enables the quantification of dispersive processes in cell biology using techniques such as moment scaling spectra.

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Crossref · 10.1016/j.jsb.2005.06.002

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Metadata

Authors
Sbalzarini, I. F., & Koumoutsakos, P.
Venue
Journal of Structural Biology
Publisher
Elsevier BV
Published
2005-08
Volume
151
Issue
2
Pages
182-195
ISSN
1047-8477 (ISSN-L)
OpenAlex
W2129140439
Cited by
1,392
References
0
FWCI
10.70
Type
journal article
Language
en
Open access
hybrid

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.