Cell Segmentation Using Level Set Methods with a New Variance Term

  • Zuzana Bílková
  • Jindřich Soukup
  • Václav Kučera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)

Abstract

We present a new method for segmentation of phase-contrast microscopic images of cells. The algorithm is based on the variational formulation of the level set method, i.e. minimizing of a functional, which describes the level set function. The functional is minimized by a gradient flow described by an evolutionary partial differential equation. The most significant new ideas are initialization using thresholding and the introduction of a new term based on local variance that speeds up convergence and achieves more accurate results. The proposed algorithm is applied on real data and compared with another algorithm. Our method yields an average gain in accuracy of 2 %.

Keywords

Segmentation Level set method Active contours Phase contrast microscopy 

Notes

Acknowledgements

The study was supported by the GAUK grant No. 914813/2013, the grant GAČR No. 13-29225S, the grant SVV-2015-260223 and the grant SVV-2016-260332. The authors would also like to thank the staff of the Working Place of Tissue Culture - Certified Laboratory at Nové Hrady for their assistance with the manual segmentation of the cells.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zuzana Bílková
    • 1
    • 2
  • Jindřich Soukup
    • 1
    • 2
  • Václav Kučera
    • 1
  1. 1.Faculty of Mathematics and PhysicsCharles University in PraguePragueCzech Republic
  2. 2.Institute of Information Theory an Automation of the ASCRPragueCzech Republic

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