title: Quantitative comparison of algorithms for tracking single fluorescent particles.
abstract: Single particle tracking has seen numerous applications in biophysics, ranging from the diffusion of proteins in cell membranes to the movement of molecular motors. A plethora of computer algorithms have been developed to monitor the sub-pixel displacement of fluorescent objects between successive video frames, and some have been claimed to have "nanometer" resolution. To date, there has been no rigorous comparison of these algorithms under realistic conditions. In this paper, we quantitatively compare specific implementations of four commonly used tracking algorithms: cross-correlation, sum-absolute difference, centroid, and direct Gaussian fit. Images of fluorescent objects ranging in size from point sources to 5 microm were computer generated with known sub-pixel displacements. Realistic noise was added and the above four algorithms were compared for accuracy and precision. We found that cross-correlation is the most accurate algorithm for large particles. However, for point sources, direct Gaussian fit to the intensity distribution is the superior algorithm in terms of both accuracy and precision, and is the most robust at low signal-to-noise. Most significantly, all four algorithms fail as the signal-to-noise ratio approaches 4. We judge direct Gaussian fit to be the best algorithm when tracking single fluorophores, where the signal-to-noise is frequently near 4.
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia 22908, USA.
authors.forename: M K
authors.initials: MK
authors.lastname: Cheezum
authors.forename: W F
authors.initials: WF
authors.lastname: Walker
authors.forename: W H
authors.initials: WH
authors.lastname: Guilford
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D003198:Computer Simulation; D005453:Fluorescence; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D009038:Motion; D010316:Particle Size; D013223:Statistics as Topic