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Automatic segmentation of cell nuclei from confocal laser scanning microscopy images

A. Kelemen, H-W. Reist, G. Gerig and G. Székely
Visualization in Biomedical Computing, Proc. VBC '96


In this paper we present a method for the fully automatic segmentation of cell nuclei from 3D confocal laser microscopy images. The method is based on the combination of previously proposed techniques which have been refined for the requirements of this task. A 3D extension of a wave propagation technique applied to gradient magnitude images allows us a precise initialization of elastically deformable Fourier models and therefore a fully automatic image analysis. The shape parameters are transformed into invariant descriptors and provide the basis of a statistical analysis of cell nucleus shapes. This analysis will be carried out in order to determine average intersection lengths between cell nuclei and single particle tracks of ionizing radiation. This allows a quantification of absorbed energy on living cells leading to a better understanding of the biological significance of exposure to radiation in low doses.

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  author = {A. Kelemen and H-W. Reist and G. Gerig and G. Székely},
  title = {Automatic segmentation of cell nuclei from confocal laser scanning microscopy images},
  booktitle = {Visualization in Biomedical Computing, Proc. VBC '96},
  year = {1996},
  pages = {193 - 202},
  editor = {Höhne K. H. and Kikinis R.},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  keywords = {segmentation, deformable models, snakes, volume images, model-based, confocal laser scanning}