Multilevel space-time aggregation for bright field cell microscopy segmentation and tracking

  • Authors:

  • Tiffany Inglis;Hans De Sterck;Geoffrey Sanders;Haig Djambazian;Robert Sladek;Saravanan Sundararajan;Thomas J. Hudson

  • Affiliations:

  • Centre for Computational Mathematics in Industry and Commerce, University of Waterloo, Waterloo, ON, Canada;Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada;Department of Applied Mathematics, University of Colorado at Boulder, Boulder, CO;McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada;McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada;McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada;Ontario Institute for Cancer Research, Toronto, ON, Canada

  • Venue:
  • Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
  • Year:
  • 2010

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Abstract

A multilevel aggregation method is applied to the problem of segmenting live cell bright field microscope images. The method employed is a variant of the so-called "Segmentation by Weighted Aggregation" technique, which itself is based on Algebraic Multigrid methods. The variant of the method used is described in detail, and it is explained how it is tailored to the application at hand. In particular, a new scale-invariant "saliency measure" is proposed for deciding when aggregates of pixels constitute salient segments that should not be grouped further. It is shown how segmentation based on multilevel intensity similarity alone does not lead to satisfactory results for bright field cells. However, the addition of multilevel intensity variance (as a measure of texture) to the feature vector of each aggregate leads to correct cell segmentation. Preliminary results are presented for applying the multilevel aggregation algorithm in space time to temporal sequences of microscope images, with the goal of obtaining space-time segments ("object tunnels") that track individual cells. The advantages and drawbacks of the space-time aggregation approach for segmentation and tracking of live cells in sequences of bright field microscope images are presented, along with a discussion on how this approach may be used in the future work as a building block in a complete and robust segmentation and tracking system.