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Automated Segmentation and Tracking of SAM Cells

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Pattern Recognition (CCPR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 484))

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Abstract

In this paper, we propose an automated segmentation and tracking system for the shoot apical meristem (SAM) cells. Cells are segmented using a mixed filter based watershed segmentation method, which is proved to be very robust and efficient. After segmentation, a Triangle Neighborhood Structure matching method is proposed to track the segmented cells across different time instances. Our tracking method reduces the dependence on neighbors, because we only need two neighbors for any cells for matching while the other local graph matching methods require a much larger number of neighbors. Using our proposed segmentation and tracking system, we are able to track 97% of the plant SAM cells.

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Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Min Liu & Peng Xiang

Authors
  1. Min Liu
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  2. Peng Xiang
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Editor information

Editors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, 410082, Changsha, P.R. China

    Shutao Li

  2. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  3. College of electrical and information engineering, Hunan University, 410082, Changsha, P.R. China

    Yaonan Wang

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© 2014 Springer-Verlag Berlin Heidelberg

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Liu, M., Xiang, P. (2014). Automated Segmentation and Tracking of SAM Cells. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_40

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  • DOI: https://doi.org/10.1007/978-3-662-45643-9_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45642-2

  • Online ISBN: 978-3-662-45643-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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