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Machine Vision and Applications

, Volume 23, Issue 4, pp 607–621 | Cite as

Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images

  • Rehan AliEmail author
  • Mark Gooding
  • Tünde Szilágyi
  • Borivoj Vojnovic
  • Martin Christlieb
  • Michael Brady
Special Issue Paper

Abstract

The detection and segmentation of adherent eukaryotic cells from brightfield microscopy images represent challenging tasks in the image analysis field. This paper presents a free and open-source image analysis package which fully automates the tasks of cell detection, cell boundary segmentation, and nucleus segmentation in brightfield images. The package also performs image registration between brightfield and fluorescence images. The algorithms were evaluated on a variety of biological cell lines and compared against manual and fluorescence-based ground truths. When tested on HT1080 and HeLa cells, the cell detection step was able to correctly identify over 80% of cells, whilst the cell boundary segmentation step was able to segment over 75% of the cell body pixels, and the nucleus segmentation step was able to correctly identify nuclei in over 75% of the cells. The algorithms for cell detection and nucleus segmentation are novel to the field, whilst the cell boundary segmentation algorithm is contrast-invariant, which makes it more robust on these low-contrast images. Together, this suite of algorithms permit brightfield microscopy image processing without the need for additional fluorescence images. Finally our sephaCe application, which is available at http://www.sephace.com, provides a novel method for integrating these methods with any motorised microscope, thus facilitating the adoption of these techniques in biological research labs.

Keywords

Segmentation Registration Cell detection Level sets Monogenic signal Continuous intrinsic dimensionality 

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Rehan Ali
    • 1
    Email author
  • Mark Gooding
    • 2
  • Tünde Szilágyi
    • 3
  • Borivoj Vojnovic
    • 4
  • Martin Christlieb
    • 4
  • Michael Brady
    • 3
  1. 1.Department of Radiation PhysicsStanford UniversityStanfordUSA
  2. 2.Mirada Medical LtdOxfordUK
  3. 3.Department of Engineering Science, FRS FREng FMedSci Wolfson Medical Vision LabUniversity of OxfordOxfordUK
  4. 4.Gray Institute for Radiation Oncology and BiologyUniversity of OxfordOxfordUK

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