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Refining particle positions using circular symmetry
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 Ecology and Environmental Sciences.
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 4, article id e0175015Article in journal (Refereed) Published
Abstract [en]

Particle and object tracking is gaining attention in industrial applications and is commonly applied in: colloidal, biophysical, ecological, and micro-fluidic research. Reliable tracking information is heavily dependent on the system under study and algorithms that correctly determine 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.

Place, publisher, year, edition, pages
2017. Vol. 12, no 4, article id e0175015
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:umu:diva-135283DOI: 10.1371/journal.pone.0175015ISI: 000399955200030OAI: oai:DiVA.org:umu-135283DiVA, id: diva2:1098736
Funder
Swedish Research Council, 2013-5379Available from: 2017-05-26 Created: 2017-05-26 Last updated: 2018-08-15Bibliographically approved
In thesis
1. Digital holography and image processing methods for applications in biophysics
Open this publication in new window or tab >>Digital holography and image processing methods for applications in biophysics
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Understanding dynamic mechanisms, morphology and behavior of bacteria are important to develop new therapeutics to cure diseases. For example, bacterial adhesion mechanisms are prerequisites for initiation of infections and for several bacterial strains this adhesion process is mediated by adhesive surface organelles, also known as fimbriae. Escherichia coli (E. coli) is a bacterium expressing fimbriae of which pathogenic strains can cause severe diseases in fluidic environments such as the urinary tract and intestine. To better understand how E. coli cells attach and remain attached to surfaces when exposed to a fluid flow using their fimbriae, experiments using microfluidic channels are important; and to assess quantitative information of the adhesion process and cellular information of morphology, location and orientation, the imaging capability of the experimental technique is vital.

In-line digital holographic microscopy (DHM) is a powerful imaging technique that can be realized around a conventional light microscope. It is a non-invasive technique without the need of staining or sectioning of the sample to be observed in vitro. DHM provides holograms containing three-dimensional (3D) intensity and phase information of cells under study with high temporal and spatial resolution. By applying image processing algorithms to the holograms, quantitative measurements can provide information of position, shape, orientation, optical thickness of the cell, as well as dynamic cell properties such as speed, growing rate, etc.

In this thesis, we aim to improve the DHM technique and develop image processing methods to track and assess cellular properties in microfluidic channels to shed light on bacterial adhesion and cell morphology. To achieve this, we implemented a DHM technique and developed image processing algorithms to provide for a robust and quantitative analysis of holograms. We improved the cell detection accuracy and efficiency in DHM holograms by developing an algorithm for detection of cell diffraction patterns. To improve the 3D detection accuracy using in-line digital holography, we developed a novel iterative algorithm that use multiple-wavelengths. We verified our algorithms using synthetic, colloidal and cell data and applied the algorithms for detecting, tracking and analysis. We demonstrated the performance when tracking bacteria with sub-micrometer accuracy and kHz temporal resolution, as well as how DHM can be used to profile a microfluidic flow using a large number of colloidal particles. We also demonstrated how the results of cell shape analysis based on image segmentation can be used to estimate the hydrodynamic force on tethered capsule-shaped cells in micro-fluidic flows near a surface.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 59
Keywords
Digital holographic microscopy, image processing, image reconstruction, bacterial adhesion, cell morphology, algorithm development, software design, quantitative measurement, microfluidics, multidisciplinary research
National Category
Biophysics Computer Vision and Robotics (Autonomous Systems)
Research subject
Signal Processing; Technical Physics
Identifiers
urn:nbn:se:umu:diva-150687 (URN)978-91-7601-915-3 (ISBN)
Public defence
2018-09-07, Naturvetarhuset, N430, Umeå, 13:15 (English)
Opponent
Supervisors
Available from: 2018-08-17 Created: 2018-08-15 Last updated: 2018-08-16Bibliographically approved

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Rodriguez, AlvaroZhang, HanqingWiklund, KristerBrodin, TomasKlaminder, JonatanAndersson, PatrikAndersson, Magnus
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