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

• Type: Conference
• Year: 2009
• Where: MICCAI
• Authors: Andreas Wimmer, Grzegorz Soza, Joachim Hornegger
• Keywords: 3-D Liver Segmentation, Medical Image Segmentation, Medical Imaging, MICCAI 2009, Probabilistic Active Shape