Paper Info
Title
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
Abstract
We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed by the authors earlier in T. Chan and L. Vese (1999. In Scale-Space'99, M. Nilsen et al. (Eds.), LNCS, vol. 1682, pp. 141–151) and T. Chan and L. Vese (2001. IEEE-IP, 10(2):266–277). The multiphase level set formulation is new and of interest on its own: by construction, it automatically avoids the problems of vacuum and overlap; it needs only log n level set functions for n phases in the piecewise constant case; it can represent boundaries with complex topologies, including triple junctions; in the piecewise smooth case, only two level set functions formally suffice to represent any partition, based on The Four-Color Theorem. Finally, we validate the proposed models by numerical results for signal and image denoising and segmentation, implemented using the Osher and Sethian level set method.
Year
DOI
Venue
2002
10.1023/A:1020874308076
International Journal of Computer Vision
Keywords
Field
DocType
energy minimization,multi-phase motion,image segmentation,level sets,curvature,PDE's,denoising,edge detection,active contours
Mumford–Shah functional,Computer science,Edge detection,Level set,Image segmentation,Artificial intelligence,Piecewise,Active contour model,Topology,Computer vision,Level set method,Segmentation,Algorithm
Journal
Volume
Issue
ISSN
50
3
1573-1405
Citations 
PageRank 
References 
969
43.52
17
Authors
2
Search Limit
100969
Name
Order
Citations
PageRank
Luminita A. Vese15389302.64
Tony F. Chan28733659.77