We introduce a segmentation framework which combines and shares advantages of both an implicit surface representation and a parametric shape model based on spherical harmonics. Besides the elegant surface representation it also inherits the power and flexibility of variational level set methods with respect to the modeling of data terms. At the same time it provides all advantages of parametric shape models such as a sparse and multiscale shape representation. Additionally, we introduce a regularizer that helps to ensure a unique decomposition into spherical harmonics and thus the comparability of parameter values of multiple segmentations. We demonstrate the benefits of our method on medical and photometric data and present two possible extensions.     «

We introduce a segmentation framework which combines and shares advantages of both an implicit surface representation and a parametric shape model based on spherical harmonics. Besides the elegant surface representation it also inherits the power and flexibility of variational level set methods with respect to the modeling of data terms. At the same time it provides all advantages of parametric shape models such as a sparse and multiscale shape representation. Additionally, we introduce a regula...     »