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A fast and robust circle detection method using isosceles triangles sampling
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.
2016 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 54, p. 218-228Article in journal (Refereed) Published
Abstract [en]

Circle detection using randomized sampling has been developed in recent years to reduce computational intensity. However, randomized sampling is sensitive to noise that can lead to reduced accuracy and false-positive candidates. To improve on the robustness of randomized circle detection under noisy conditions this paper presents a new methodology for circle detection based upon randomized isosceles triangles sampling. It is shown that the geometrical property of isosceles triangles provides a robust criterion to find relevant edge pixels which, in turn, offers an efficient means to estimate the centers and radii of circles. For best efficiency, the estimated results given by the sampling from individual connected components of the edge map were analyzed using a simple clustering approach. To further improve on the accuracy we applied a two-step refinement process using chords and linear error compensation with gradient information of the edge pixels. Extensive experiments using both synthetic and real images have been performed. The results are compared to leading state-of-the-art algorithms and it is shown that the proposed methodology has a number of advantages: it is efficient in finding circles with a low number of iterations, it has high rejection rate of false-positive circle candidates, and it has high robustness against noise. All this makes it adaptive and useful in many vision applications.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 54, p. 218-228
Keywords [en]
Circle detection, Randomized algorithm, Sampling strategy, Isosceles triangles
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:umu:diva-112312DOI: 10.1016/j.patcog.2015.12.004ISI: 000372380700017OAI: oai:DiVA.org:umu-112312DiVA, id: diva2:877153
Funder
Swedish Research Council, 2013-5379Available from: 2015-12-05 Created: 2015-12-05 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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Hanqing, ZhangWiklund, KristerAndersson, Magnus

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