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Segmentation of Clustered Cells in Microscopy Images by Geometric PDEs and Level Sets

  • A. KuijperEmail author
  • B. Heise
  • Y. Zhou
  • L. He
  • H. Wolinski
  • S. Kohlwein

Abstract

With the huge amount of cell images produced in bio-imaging, automatic methods for segmentation are needed in order to evaluate the content of the images with respect to types of cells and their sizes. Traditional PDE-based methods using level-sets can perform automatic segmentation, but do not perform well on images with clustered cells containing sub-structures. We present two modifications for popular methods and show the improved results.

Keywords

Automatic Segmentation Differential Interference Contrast Active Contour Model Cell Segmentation Differential Interference Contrast Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work was partially supported by the mYeasty pilot-project by the Austrian GEN_AU research program (www.gen-au.at). It was carried out when A. Kuijper, Y. Zhou, and L. He were with the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz, Austria.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • A. Kuijper
    • 1
    Email author
  • B. Heise
    • 2
  • Y. Zhou
    • 3
  • L. He
    • 4
  • H. Wolinski
    • 5
  • S. Kohlwein
    • 5
  1. 1.Fraunhofer IGD, Institute for Computer Graphics Research, Department of Computer ScienceTU DarmstadtDarmstadtGermany
  2. 2.Department of Knowledge-Based Mathematical SystemsJohannes Kepler UniversityLinzAustria
  3. 3.Department of Virtual DesignSiemens AGMunichGermany
  4. 4.Luminescent Technologies Inc.Palo AltoUSA
  5. 5.SFB Biomembrane Research Center, Institute of Molecular Biosciences, Department BiochemistryUniversity of GrazGrazAustria

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