Active shape models—their training and application
Computer Vision and Image Understanding
International Journal of Computer Vision
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
Active Contours Using a Constraint-Based Implicit Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Level Set Active Contours on Unstructured Point Cloud
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
IEEE Transactions on Image Processing
Joint thrombus and vessel segmentation using dynamic texture likelihoods and shape prior
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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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.