Iterated random walks with shape prior

Esmeralda Ruiz Pujadas, Hans Martin Kjer, Gemma Piella, Miguel Angel González Ballester

Research output: Contribution to journalJournal articleResearchpeer-review


We propose a new framework for image segmentation using random walks where a distance shape prior is combined with a region term. The shape prior is weighted by a confidence map to reduce the influence of the prior in high gradient areas and the region term is computed with k-means to estimate the parametric probability density function. Then, random walks is performed iteratively aligning the prior with the current segmentation in every iteration. We tested the proposed approach with natural and medical images and compared it with the latest techniques with random walks and shape priors. The experiments suggest that this method gives promising results for medical and natural images.
Original languageEnglish
JournalImage and Vision Computing
Pages (from-to)12-21
Publication statusPublished - 2016


  • Random walks
  • Segmentation
  • Shape prior
  • Iterative segmentation
  • Distance map prior

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