Model-based image interpretation using genetic algorithms

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Abstract

We describe the application of genetic algorithms in model-based image interpretation. The delineation of left ventricular boundaries in apical 4-chamber echocardiograms is used as an illustrative exemplar. The suitability of genetic algorithms for the modellobjective-function/search procedure is presented.

References (11)

  • H Yamada et al.

    Recognition of echocardiograms by a dynamic programming matching method

    Patt. Recogn.

    (1991)
  • W.E.L. Grimson

    Object Recognition by Computer: The Role of Geometric Constraints

    (1990)
  • J.H. Holland

    Adaptation in Natural and Artificial Systems

    (1975)
  • L Davis

    Genetic Algorithms and Simulated Annealing

    (1987)
  • D.E. Goldberg

    Genetic Algorithms in Search, Optimization and Machine Learning

    (1989)
There are more references available in the full text version of this article.

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