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IEEE Transactions on Image Processing 17, 5 (2008) 767-779
Self-repelling snakes for topology-preserving segmentation models
Carole Le Guyader 1, 2, Luminita A. Vese 3
(2008)

The implicit framework of the level-set method has several advantages when tracking propagating fronts. Indeed, the evolving contour is embedded in a higher dimensional level-set function and its evolution can be phrased in terms of a Eulerian formulation. The ability of this intrinsic method to handle topological changes (merging and breaking) makes it useful in a wide range of applications (fluid mechanics, computer vision) and particularly in image segmentation, the main subject of this paper. Nevertheless, in some applications, this topological flexibility turns out to be undesirable: for instance, when the shape to be detected has a known topology, or when the resulting shape must be homeomorphic to the initial one. The necessity of designing topology-preserving processes arises in medical imaging, for example, in the human cortex reconstruction. It is known that the human cortex has a spherical topology so throughout the reconstruction process this topological feature must be preserved. Therefore, we propose in this paper a segmentation model based on an implicit level-set formulation and on the geodesic active contours, in which a topological constraint is enforced.
1 :  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
2 :  Institut National des Sciences Appliquées de Rennes (INSA Rennes)
Institut National des Sciences Appliquées (INSA) - Rennes
3 :  UCLA (UCLA)
California Institute of Technology
Mathématiques/Analyse numérique
additive operator splitting (AOS) scheme – geodesic active contours – level-set method – segmentation – topology preservation