Quantitative comparison of algorithms for tracking single fluorescent particles.

By M. Cheezum, W. Walker, W. Guilford. Biophysical Journal, 2001

897 citations recorded by the source corpus.

Subfields: Biophysics, Microbiology, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/8a460f417cfee378b1f8802419fc0d4f20527a05

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Citations in the corpus

Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes (2008, methodology): “Particle detection is classically performed using a bi-dimensional Gaussian approximating the microscope PS”
Precise nanometer localization analysis for individual fluorescent probes. (2002, methodology): “In addition, Cheezum et al. (2001) have written a comparison of four algorithms used in particle tracking.”
Robust single particle tracking in live cell time-lapse sequences (2008, background): “Under such conditions, particle tracking is indeed reduced to a simple particle detection and localization proble”
Feature point tracking and trajectory analysis for video imaging in cell biology. (2005, methodology): “These would be inappropriate (Cheezum et al., 2001) since the stronger noise on the bright blobs is the one that actually influences the feature point detection and causes its inaccuracy.”
Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy (2010, background): “The average SNR for each of the images is shown in Fig. 7. Here, SNR is defined as the ratio of the difference between the mean intensity of the foreground and of the background to the standard deviation of intensity in the foreground region, which is appropriate in our case of signal-dependent (Poi”
Fluorophore localization algorithms for super-resolution microscopy (2014, methodology): “Localizing fluorescent probes predates super-resolution microscopy 15,16 and builds on prior work in astronomy and other fields 17,18 ; some current algorithms 19 are even inspired by global positioning systems 20 .”
SINGLE-PARTICLE TRACKING (2009, background): “The effect of noise is discussed in detail in [70,75–77].”
Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics (2005, background): “” A recent study [4] compares the performance of such algorithms.”
Tracking in cell and developmental biology. (2009, methodology): “Experiments have shown superior performance of Gaussian fitting (with a limit of SNR≈ 4) [64] and successful application to the detection of single [65] as well as clustered particles (using Gaussian mixture models) [66].”