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A Machine Vision Framework for Automated Localization of Microinjection Sites on Low-Contrast Single Adherent Cells

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Image Analysis and Recognition (ICIAR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6754))

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Abstract

To perform high-throughput single-cell studies, automation of the potential experiments is quite necessary. Due to their complex morphology, automatic manipulation and visual analysis of adherent cells which include a wide range of mammalian cell lines is a challenging task. In this paper, the problem of adherent cells localization for the purpose of automated robotic microinjection has been stated and a practical two-stage texture-based solution has been proposed. The method has been tested on NIH/3T3 cells and the results have been reported.

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Author information

Authors and Affiliations

  1. School of Engineering Science, Simon Fraser University, Surrey, BC, Canada

    Hadi Esmaeilsabzali, Kelly Sakaki & Edward J. Park

  2. Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada

    Nikolai Dechev

  3. Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada

    Robert D. Burke

Authors
  1. Hadi Esmaeilsabzali
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  2. Kelly Sakaki
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  3. Nikolai Dechev
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  4. Robert D. Burke
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  5. Edward J. Park
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2011 Springer-Verlag Berlin Heidelberg

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Esmaeilsabzali, H., Sakaki, K., Dechev, N., Burke, R.D., Park, E.J. (2011). A Machine Vision Framework for Automated Localization of Microinjection Sites on Low-Contrast Single Adherent Cells. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_2

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  • DOI: https://doi.org/10.1007/978-3-642-21596-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21595-7

  • Online ISBN: 978-3-642-21596-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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