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Automatic Grid Fitting for Genetic Spot Array Images Containing Guide Spots

  • Norbert Brändle
  • Hilmar Lapp
  • Horst Bischof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1689)

Abstract

In the domain of biotechnology array-based methods are used to gain rapid access to genetic information based on the signals of the individual array elements (spots). For an automated analysis of the spots it is necessary to fit a grid to the spots in the digital image in order to represent the array distortions that may occur in the course of the experiment. In order to make the grid fitting problem tractable in a certain class of experiments spot arrays contain a sub-grid of guide spots with a known signal characteristic. We present an automatic grid fitting method for spot array images containing guide spots. Our approach uses simple image processing methods and takes into account prior knowledge inherent in the imaging process.

Keywords

Genomics Spot Array Images Grid Fitting 

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References

  1. 1.
    N. Brändle, H. Lapp, and H. Bischof. Fully Automatic Grid Fitting for Genetic Spot Array Images Containing Guide Spots. Technical Report PRIP-TR-058, PRIP, TU Wien, 1999.Google Scholar
  2. 2.
    M. Chee et al. Accessing Genetic Information with High-Density DNA Arrays. Science, 274:610–614, 1996.CrossRefGoogle Scholar
  3. 3.
    R. J. Johnston et al. Autoradiography using storage phosphor technology. Electrophoresis, 11:355–360, 1990.CrossRefGoogle Scholar
  4. 4.
    S. Meier-Ewert et al. An automated approach to generating expressed sequencecatalogues. Nature, 361:375–376, 1993.CrossRefGoogle Scholar
  5. 5.
    K. Hartelius. Analysis of Irregularly Distributed Points. PhD thesis, Institute of Mathematical Modelling, Technical University of Denmark, 1996.Google Scholar
  6. 6.
    Anil K. Jain. Fundamentals of Digital Image Processing. Prentice-Hall, 1986.Google Scholar
  7. 7.
    E. Lander. The new genomics: Global views of biology. Science, 274:536–539, 1996.CrossRefGoogle Scholar
  8. 8.
    Benjamin Lewin. Genes VI. Oxford University Press, 1997.Google Scholar
  9. 9.
    Shree K. Nayar and Tomaso Poggio, editors. Early Visual Learning. Oxford University Press, 1996.Google Scholar
  10. 10.
    H. J. Noordmans and A. W. M. Smeulders. Detection and Characterization of Isolated and Overlapping Spots. Computer Vision and Image Understanding, 70(1):23–35, 1998.CrossRefGoogle Scholar
  11. 11.
    William H. Press et al. Numerical Recipes in C. Cambridge University Press, 1992.Google Scholar
  12. 12.
    Gerhard Winkler. Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer Verlag, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Norbert Brändle
    • 1
  • Hilmar Lapp
    • 2
  • Horst Bischof
    • 1
  1. 1.Pattern Recognition and Image Processing GroupVienna University of TechnologyViennaAustralia
  2. 2.Novartis Forschungsinstitut GeneticsViennaAustralia

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