Iterated random walks with shape prior *

• New framework using random walks combining a distance-based prior with a region term
• Prior weighted by a confidence map to reduce influence of the prior in certain areas
• A refinement might be applied using a narrow band.
• Our approach was tested with natural and medical images giving satisfactory results.

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.