Active contours without edges

Tony Chan, Luminita A. Vese

Research output: Contribution to journalArticlepeer-review

8443 Scopus citations

Abstract

In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a `mean-curvature flow'-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.

Original languageEnglish (US)
Pages (from-to)266-277
Number of pages12
JournalIEEE Transactions on Image Processing
Volume10
Issue number2
DOIs
StatePublished - Feb 1 2001

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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