A robust particle detection algorithm based on symmetry

Particle tracking is common in many biophysical, ecological, and micro-fluidic applications. Reliable tracking information is heavily dependent on of the system under study and algorithms that correctly determines particle position between images. However, in a real environmental context with the presence of noise including particular or dissolved matter in water, and low and fluctuating light conditions, many algorithms fail to obtain reliable information. We propose a new algorithm, the Circular Symmetry algorithm (C-Sym), for detecting the position of a circular particle with high accuracy and precision in noisy conditions. The algorithm takes advantage of the spatial symmetry of the particle allowing for subpixel accuracy. We compare the proposed algorithm with four different methods using both synthetic and experimental datasets. The results show that C-Sym is the most accurate and precise algorithm when tracking micro-particles in all tested conditions and it has the potential for use in applications including tracking biota in their environment.

[1]  Kim Sneppen,et al.  DNA supercoiling enhances cooperativity and efficiency of an epigenetic switch , 2013, Proceedings of the National Academy of Sciences.

[2]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[3]  Joseph J. Loparo,et al.  Tethered particle motion with single DNA molecules , 2015 .

[4]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[5]  Cuneyt Akinlar,et al.  EDCircles: A real-time circle detector with a false detection control , 2013, Pattern Recognit..

[6]  Carlo Manzo,et al.  Quantitative analysis of DNA-looping kinetics from tethered particle motion experiments. , 2010, Methods in enzymology.

[7]  Cam Tropea,et al.  High-precision sub-pixel interpolation in particle image velocimetry image processing , 2005 .

[8]  S. Wereley,et al.  Particle imaging techniques for microfabricated fluidic systems , 2003 .

[9]  J. Käs,et al.  Apparent subdiffusion inherent to single particle tracking. , 2002, Biophysical journal.

[10]  S. Schedin,et al.  Cell shape identification using digital holographic microscopy. , 2015, Applied optics.

[11]  Patrenahalli M. Narendra,et al.  A Separable Median Filter for Image Noise Smoothing , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Stanley Brown,et al.  Tethered particle analysis of supercoiled circular DNA using peptide nucleic acid handles , 2014, Nature Protocols.

[13]  C. Tardin,et al.  High-throughput single-molecule analysis of DNA–protein interactions by tethered particle motion , 2012, Nucleic acids research.

[14]  Magnus Andersson,et al.  A fast and robust circle detection method using isosceles triangles sampling , 2016, Pattern Recognit..

[15]  Cees Dekker,et al.  Non-bias-limited tracking of spherical particles, enabling nanometer resolution at low magnification. , 2012, Biophysical journal.

[16]  Lene B. Oddershede,et al.  Optimizing active and passive calibration of optical tweezers , 2011 .

[17]  J. Koch,et al.  Geometric Hermite interpolation with maximal orderand smoothness , 1996, Comput. Aided Geom. Des..

[18]  K. Jacobson,et al.  Single-particle tracking: applications to membrane dynamics. , 1997, Annual review of biophysics and biomolecular structure.

[19]  Keith A. Lidke,et al.  Fast, single-molecule localization that achieves theoretically minimum uncertainty , 2010, Nature Methods.

[20]  Jerry Chao,et al.  Localization accuracy in single molecule microscopy using electron-multiplying charge-coupled device cameras , 2012, Photonics West - Biomedical Optics.

[21]  Charlie Gosse,et al.  Magnetic tweezers: micromanipulation and force measurement at the molecular level. , 2002, Biophysical journal.

[22]  M. Sheetz,et al.  Tracking kinesin-driven movements with nanometre-scale precision , 1988, Nature.

[23]  M K Cheezum,et al.  Quantitative comparison of algorithms for tracking single fluorescent particles. , 2001, Biophysical journal.

[24]  Po-Ying Chen,et al.  Precision tracking control of a biaxial piezo stage using repetitive control and double-feedforward compensation , 2011 .

[25]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[26]  W. Webb,et al.  Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules. , 1994, Biophysical journal.

[27]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[28]  E. Stelzer.  Light microscopy: Beyond the diffraction limit? , 2002, Nature.

[29]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..