Back to Search
Start Over
A Fast Level Set-Like Algorithm with Topology Preserving Constraint
- Source :
- Computer Analysis of Images and Patterns ISBN: 9783642037665, CAIP
- Publication Year :
- 2009
- Publisher :
- Springer Berlin Heidelberg, 2009.
-
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.
- Subjects :
- Active contour model
Computer science
Image processing
010103 numerical & computational mathematics
02 engineering and technology
Image segmentation
Topology
Real image
01 natural sciences
Level set
0202 electrical engineering, electronic engineering, information engineering
Digital geometry
020201 artificial intelligence & image processing
Point (geometry)
0101 mathematics
Parametrization
Algorithm
Parametric statistics
Subjects
Details
- ISBN :
- 978-3-642-03766-5
- ISBNs :
- 9783642037665
- Database :
- OpenAIRE
- Journal :
- Computer Analysis of Images and Patterns ISBN: 9783642037665, CAIP
- Accession number :
- edsair.doi...........f445ac3ce7d009838489b6d4d77162ad
- Full Text :
- https://doi.org/10.1007/978-3-642-03767-2_113