Statistical shape priors for level set segmentation

Starting in the early 1990's level set methods have become a popular mathematical framework for variational image segmentation. In many applications of segmentation, however, cost functionals which merely take into account the intensity information of the input image will not give rise to the desired segmentation results. To cope with missing or misleading image information, researchers have proposed to impose statistical shape priors into the segmentation process. Such shape priors favor the evolving embedding function to remain similar to embedding functions associated with a collection of training shapes. As a consequence, one can obtain shape-consistent segmentation despite large amounts of noise, background clutter and partial occlusion of the object of interest. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)