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A Fast Level Set-Like Algorithm with Topology Preserving Constraint

  • Martin Maška
  • Pavel Matula
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)

Abstract

Implicit active contours are widely employed in image processing and related areas. Their implementation using the level set framework brings several advantages over parametric snakes. In particular, a parametrization independence, topological flexibility, and straightforward extension into higher dimensions have led to their popularity. However, in some applications the topological flexibility of the implicit contour is not desirable. Imposing topology-preserving constraints on evolving contours is often more convenient than including additional postprocessing steps. In this paper, we build on the work by Han et al. [1] introducing a topology-preserving extension of the narrow band algorithm involving simple point concept from digital geometry. In order to significantly increase computational speed, we integrate a fast level set-like algorithm by Nilsson and Heyden [2] with the simple point concept to obtain a fast topology-preserving algorithm for implicit active contours. The potential of the new algorithm is demonstrated on both synthetic and real image data.

Keywords

Active Contour Active Contour Model Simple Point Pattern Recognition Letter Geodesic Active Contour 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martin Maška
    • 1
  • Pavel Matula
    • 1
  1. 1.Centre for Biomedical Image Analysis, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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