An Active Contour Model without Edges

  • Tony Chan
  • Luminita Vese
Conference paper

DOI: 10.1007/3-540-48236-9_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1682)
Cite this paper as:
Chan T., Vese L. (1999) An Active Contour Model without Edges. In: Nielsen M., Johansen P., Olsen O.F., Weickert J. (eds) Scale-Space Theories in Computer Vision. Scale-Space 1999. Lecture Notes in Computer Science, vol 1682. Springer, Berlin, Heidelberg

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 gra- dient. The model is a combination between more classical active contour models using mean curvature motion techniques, and the Mumford-Shah model for segmentation. We minimize an energy which can be seen as a particular case of the so-called 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. 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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Tony Chan
    • 1
  • Luminita Vese
    • 1
  1. 1.Department of MathematicsUniversity of California, Los AngelesLos Angeles

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