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Automatic construction of eigenshape models by Genetic Algorithm

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

A new approach to the problem of automatic construction of eigenshape models is presented. Eigenshape models have proved to be successful in a variety of medical image analysis problems. However, automatic model construction is a difficult problem, and in many applications the models are built by hand — a painstaking process. Previous attempts to produce models automatically have been applicable only in specific cases or under certain assumptions. We show that the problem can be understood very simply in terms of shape symmetries. The pose and parameterisation of each shape must be chosen so as to produce a model that is compact and specific. We define an objective function that measures these properties. The problem of automatic model construction is thus reduced to an optimisation problem. We show that the objective function we define can be optimised by a Genetic Algorithm, and produces models that are better than hand built ones.

Keywords

  • Genetic Algorithm
  • Symmetry Transformation
  • Bezier Curve
  • Active Shape Model
  • Genetic Algorithm Parameter

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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© 1997 Springer-Verlag Berlin Heidelberg

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Kotcheff, A.C.W., Taylor, C.J. (1997). Automatic construction of eigenshape models by Genetic Algorithm. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_1

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  • DOI: https://doi.org/10.1007/3-540-63046-5_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63046-3

  • Online ISBN: 978-3-540-69070-2

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