Global-to-local shape priors for variational image segmentation

Abstract

One major problem, when using statistical shape information in image segmentation problems, is that many training samples are needed in order to obtain a satisfactory shape prior for a particular class, especially when the intra-class variability of the object shapes is high. To cope with this problem, we present an elegant variational formulation that allows local adaptations of the parameters associated with a trained shape prior. This enables us to obtain accurate segmentation results with a limited amount of training shapes. We provide a sound mathematical foundation for our approach and embed it into the well-known level set segmentation framework, which makes our approach applicable to a large class of problems. Moreover, we show how a smooth transition from global to local adaptations of the shape parameters can be achieved. We point out the advantages of our new variational global-to-local approach by comparing it with another level set segmentation approach that includes a global shape prior.