Automatic Clump Splitting for Cell Quantification in Microscopical Images

  • Gloria Díaz
  • Fabio Gonzalez
  • Eduardo Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


This paper presents an original method for splitting overlapped cells in microscopical images, based on a template matching strategy. First,a single template cell is estimated using an Expectation Maximization algorithm applied to a collection of correctly segmented cells from the original image. Next, a process based on matching the template against the clumped shape and removing the matched area is applied iteratively. A chain code representation is used for establishing best correlation between these two shapes. Maximal correlation point is used as an landmark point for the registration approach, which finds the affine transformation that maximises the intersection area between both shapes. Evaluation was carried out on 18 images in which 52 clumped shapes were present. The number of found cells was compared with the number of cells counted by an expert and results show agreement on a \(93\:\%\) of the cases.


Cell quantification Overlapping objects Segmentation  Clump splitting 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gloria Díaz
    • 1
  • Fabio Gonzalez
    • 1
  • Eduardo Romero
    • 1
  1. 1.Bioingenium Research Group, National University of Colombia, BogotáColombia

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