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UmUTracker: a versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
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2017 (English)In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 219, 390-399 p.Article in journal (Refereed) Published
Abstract [en]

We present a versatile and fast MATLAB program (UmUTracker) that automatically detects and tracks particles by analyzing video sequences acquired by either light microscopy or digital in-line holographic microscopy. Our program detects the 2D lateral positions of particles with an algorithm based on the isosceles triangle transform, and reconstructs their 3D axial positions by a fast implementation of the Rayleigh-Sommerfeld model using a radial intensity profile. To validate the accuracy and performance of our program, we first track the 2D position of polystyrene particles using bright field and digital holographic microscopy. Second, we determine the 3D particle position by analyzing synthetic and experimentally acquired holograms. Finally, to highlight the full program features, we profile the microfluidic flow in a 100 gm high flow chamber. This result agrees with computational fluid dynamic simulations. On a regular desktop computer UmUTracker can detect, analyze, and track multiple particles at 5 frames per second for a template size of 201 x 201 in a 1024 x 1024 image. To enhance usability and to make it easy to implement new functions we used object-oriented programming. UmUTracker is suitable for studies related to: particle dynamics, cell localization, colloids and microfluidic flow measurement.

Program summary

Program title: UmUTracker Program Files doi: http://dx.doi.org/10.17632/fkprs4s6xp.1

Licensing provisions: Creative Commons by 4.0 (CC by 4.0)

Programming language: MATLAB Nature of problem: 3D multi-particle tracking is a common technique in physics, chemistry and biology. However, in terms of accuracy, reliable particle tracking is a challenging task since results depend on sample illumination, particle overlap, motion blur and noise from recording sensors. Additionally, the computational performance is also an issue if, for example, a computationally expensive process is executed, such as axial particle position reconstruction from digital holographic microscopy data. Versatile robust tracking programs handling these concerns and providing a powerful post-processing option are significantly limited.

Solution method: UmUTracker is a multi-functional tool to extract particle positions from long video sequences acquired with either light microscopy or digital holographic microscopy. The program provides an easy-to-use graphical user interface (GUI) for both tracking and post-processing that does not require any programming skills to analyze data from particle tracking experiments. UmUTracker first conduct automatic 2D particle detection even under noisy conditions using a novel circle detector based on the isosceles triangle sampling technique with a multi-scale strategy. To reduce the computational load for 3D tracking, it uses an efficient implementation of the Rayleigh-Sommerfeld light propagation model. To analyze and visualize the data, an efficient data analysis step, which can for example show 4D flow visualization using 3D trajectories, is included. Additionally, UmUTracker is easy to modify with user customized modules due to the object-oriented programming style.

Additional comments: Program obtainable from https://sourceforge.net/projects/umutracker/

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 219, 390-399 p.
Keyword [en]
Image processing, Digital holographic microscopy, Particle tracking velocimetry, Microfluidics
National Category
Computer Vision and Robotics (Autonomous Systems) Other Physics Topics
Identifiers
URN: urn:nbn:se:umu:diva-131047DOI: 10.1016/j.cpc.2017.05.029ISI: 000407984100035OAI: oai:DiVA.org:umu-131047DiVA: diva2:1071237
Available from: 2017-02-03 Created: 2017-02-03 Last updated: 2018-01-13Bibliographically approved

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Zhang, HanqingStangner, TimWiklund, KristerRodrigues, AlvaroAndersson, Magnus
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