Title
Self-Repelling Snakes for Topology-Preserving Segmentation Models
Abstract
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.
Year
DOI
Venue
2008
10.1109/TIP.2008.919951
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Keywords
Field
DocType
human cortex reconstruction,human cortex,topological change,topological constraint,topological flexibility,topology-preserving segmentation models,implicit level-set formulation,higher dimensional level-set function,topological feature,level-set method,self-repelling snakes,eulerian formulation,computer vision,topology,computer simulation,shape,edge detection,image reconstruction,artificial intelligence,segmentation,algorithms,application software,biomedical imaging,level set method,image segmentation,level set,merging,process design,fluid mechanics
Active contour model,Iterative reconstruction,Topology,Computer vision,Segmentation,Level set method,Edge detection,Image segmentation,Artificial intelligence,Geodesic,Mathematics,Homeomorphism
Journal
Volume
Issue
ISSN
17
5
1057-7149
Citations 
PageRank 
References 
25
0.93
19
Authors
2
Name
Order
Citations
PageRank
carole le guyader112614.01
Luminita A. Vese25389302.64