Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional

Daniel Cremers, Florian Tischhäuser, Joachim Weickert, Christoph Schnörr

Research output: Contribution to journalArticlepeer-review

263 Scopus citations


We present a modification of the Mumford-Shah functional and its cartoon limit which facilitates the incorporation of a statistical prior on the shape of the segmenting contour. By minimizing a single energy functional, we obtain a segmentation process which maximizes both the grey value homogeneity in the separated regions and the similarity of the contour with respect to a set of training shapes. We propose a closed-form, parameter-free solution for incorporating invariance with respect to similarity transformations in the variational framework. We show segmentation results on artificial and real-world images with and without prior shape information. In the cases of noise, occlusion or strongly cluttered background the shape prior significantly improves segmentation. Finally we compare our results to those obtained by a level set implementation of geodesic active contours.

Original languageEnglish
Pages (from-to)295-313
Number of pages19
JournalInternational Journal of Computer Vision
Issue number3
StatePublished - Dec 2002
Externally publishedYes


  • Diffusion snake
  • Geodesic active contours
  • Image segmentation
  • Shape recognition
  • Statistical learning
  • Variational methods


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