Segmentation and shape tracking of whole fluorescent cells based on the Chan-Vese model Articles uri icon

publication date

  • June 2013

start page

  • 995

end page

  • 1006

issue

  • 6

volume

  • 32

International Standard Serial Number (ISSN)

  • 0278-0062

Electronic International Standard Serial Number (EISSN)

  • 1558-0062

abstract

  • Abstract: We present a fast and robust approach to tracking the evolving shape of whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the cell boundaries are detected by minimizing the Chan-Vese model in the fast level set-like and graph cut frameworks. To allow simultaneous tracking of multiple cells over time, both frameworks have been integrated with a topological prior exploiting the object indication function. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively.

keywords

  • shape; minimization; level set; heuristic algorithms; target tracking; image segmentation; cell tracking; fluorescence microscopy; graph cut optimization; level set framework; adipose-derived mesenchymal stem cells; cell tracking; coherence-enhancing diffusion; graph cut; graph cut frameworks; level set framework; squamous cell carcinoma; cell culture; fluorescence; fluorescence microscopy; graphic methods; cells; accuracy; animal cell; article; biological model; cell lineage; cell maturation; cell shape; cell tracking; chan vese model; diffusion; human; human cell; lung squamous cell carcinoma; mesenchymal stem cell; nonhuman; rat; three dimensional imaging; time lapse imaging; whole cell; whole fluorescent cell; microfiltration; noise measurement; time-lapse video microscopy; animal; cell nucleus; chemistry; cytology; image processing; mesenchymal stroma cell; physiology; procedures; tumor cell line; image processing; methodology; animals; humans; computer assisted; mesenchymal stromal cells; microscopy; fluorescence; rats; humans