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Numerical Algorithms 48, 1-3 (2008) 189-211
Generalized Fast Marching Method: Applications to Image Segmentation
Nicolas Forcadel 1, Carole Le Guyader 2, 3, 4, Christian Gout 2, 5
(2008)

In this paper, we propose a segmentation method based on the generalized fast marching method (GFMM) developed by Carlini et al. (submitted). The classical fast marching method (FMM) is a very efficient method for front evolution problems with normal velocity (see also Epstein and Gage, The curve shortening flow. In: Chorin, A., Majda, A. (eds.) Wave Motion: Theory, Modelling and Computation, 1997) of constant sign. The GFMM is an extension of the FMM and removes this sign constraint by authorizing time-dependent velocity with no restriction on the sign. In our modelling, the velocity is borrowed from the Chan-Vese model for segmentation (Chan and Vese, IEEE Trans Image Process 10(2):266-277, 2001). The algorithm is presented and analyzed and some numerical experiments are given, showing in particular that the constraints in the initialization stage can be weakened and that the GFMM offers a powerful and computationally efficient algorithm.
1 :  COMMANDS (INRIA Saclay - Ile de France)
INRIA – CNRS : UMR7641 – Polytechnique - X – ENSTA ParisTech
2 :  Laboratoire de Mathématiques de l'INSA Rouen
Aucune
3 :  Institut National des Sciences Appliquées de Rennes (INSA Rennes)
Institut National des Sciences Appliquées (INSA) - Rennes
4 :  Institut de Recherche Mathématique de Rennes (IRMAR)
CNRS : UMR6625 – Université de Rennes 1 – École normale supérieure de Cachan - ENS Cachan – Institut National des Sciences Appliquées (INSA) : - RENNES – Université de Rennes II - Haute Bretagne
5 :  Laboratoire de Mathématiques et leurs Applications de Valenciennes, EA 45 (LAMAV)
Université de Valenciennes et du Hainaut-Cambresis – CNRS : FRE2956
Mathématiques/Analyse numérique
Fast marching method – Level set methods – Chan-Vese model for segmentation