A Generic Probabilistic Active Shape Model for Organ Segmentation

Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g. normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates. In particular, a nearest neighbor boundary appearance model is complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation. Image and shape terms are integrated into a single level set equation. Our approach has been evaluated for 3-D liver segmentation using a public data base originating from a competition (

http://sliver07.org

). With an average surface distance of 1.0 mm and an average volume overlap error of 6.5 %, it outperforms other automatic methods and provides accuracy close to interactive ones. Since no adaptions specific to liver segmentation have been made, our probabilistic active shape model can be applied to other segmentation tasks easily.