Fast Globally Optimal Segmentation of Cells in Fluorescence Microscopy Images

  • Jan-Philip Bergeest
  • Karl Rohr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6891)


Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.


Segmentation Result Active Contour Fluorescence Microscopy Image Energy Functional Intensity Inhomogeneity 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jan-Philip Bergeest
    • 1
  • Karl Rohr
    • 1
  1. 1.BIOQUANT, IPMB, and DKFZ Heidelberg Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision GroupUniversity of HeidelbergGermany

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