Towards Automatic Segmentation of Serial High-Resolution Images

  • Cornelia Brüß
  • Marc Strickert
  • Udo Seiffert
Part of the Informatik aktuell book series (INFORMAT)


Developing barley grains are to be visualised by a 4-D model, in which spatiotemporal experimental data can be integrated. The most crucial task lies in the automation of the extensive segmentation procedure. Because of constraints like incomplete a-priori expert knowledge and the complexity of this specific segmentation task, learning techniques like Artificial Neural Networks (ANN) yield promising solutions. In this work we present our first good segmentation results. Two different supervised trained ANN classifiers were applied, on one hand, the well-established borderline-learning Multiple-Layer Perceptron (MLP) and on the other hand, the prototype-based Supervised Relevance Neural Gas (SRNG). While so far segmentation was mainly achieved using manual tools, now almost automatic segmentation becomes more feasible.


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  1. 1.
    Gubatz S, et al. Three-dimensional models of developing barley seeds for the visualization of gene-expression patterns. in preparation 2006.Google Scholar
  2. 2.
    Stalling D, Westerhoff M, Hege HC. Amira: A Highly Interactive System for Visual Data Analysis. In: The Visualization Handbook; 2005. p. 749–767.Google Scholar
  3. 3.
    Dercksen VJ, Prohaska S, Hege HC. Fast cross-sectional display of large data sets. In: Proceedings IAPR Conference on Machine Vision Applications; 2005. p. 336–339.Google Scholar
  4. 4.
    Suykens JAK, Vandewalle JPL, De Moor BLR. Artificial Neural Networks for Modelling and Control of Non-Linear Systems. Kluwer Academic Publishers, Den Haag, The Netherlands; 1996.Google Scholar
  5. 5.
    Forbes N. Imitation of Life — How Biology is Inspiring Computing. The MIT Press, Cambridge, USA; 2004.Google Scholar
  6. 6.
    Minsky M, Papert S. Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge; 1969.zbMATHGoogle Scholar
  7. 7.
    Lippmann RP. An Introduction to Computing with Neural Nets. IEEE ASSP Magazine 1987;4(87):4–23.CrossRefGoogle Scholar
  8. 8.
    Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition; 1986. p. 318–362.Google Scholar
  9. 9.
    Hammer B Strickert M Villmann T Supervised neural gas with general similarity measure. Neural Processing Letters 2005;21:21–44.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cornelia Brüß
    • 1
  • Marc Strickert
    • 1
  • Udo Seiffert
    • 1
  1. 1.Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK)Gatersleben

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