A classification problem in medical radioscintigraphy

  • Georg Walch
Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3)


While digital image processing in nuclear medicine until recently consisted in a quality improvement by various filter methods, but left the recognition and classification fully to the human inspection and experience, the proposed test is a step towards automatic pattern recognition and classification. Its practical application is still in the beginning stage, but the success with simulated images lets us assume that it will be helpful in the clinical work.


Likelihood Ratio Test Test Area Digital Image Processing Simulated Image Scintigraphic Image 
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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  1. (1).
    B.W. Lindgren: Statistical Theory, Macmillan, London (1968)Google Scholar
  2. (2).
    P. Pistor, G. Walch, H.G. Meder, W.A. Hunt, W.J. Lorenz, A. Amann, P. Georgi, H. Luig, P. Schmidlin, H. Wiebelt: Digital Image Processing in Nuclear Medicine, Kerntechnik 14, 299–306, and 353–359 (1972)Google Scholar
  3. (3).
    P. Pistor, P. Georgi, G. Walch: The Heidelberg Scintigraphic Image Processing System, Proc. 2. Symposium on Sharing of Computer Programs and Technology in Nuclear Medicine, Oak Ridge National Laboratory (1972)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1973

Authors and Affiliations

  • Georg Walch
    • 1
  1. 1.IBM Heidelberg Scientific CenterGermany

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