Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework
International Journal of Computer Vision
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
International Journal of Computer Vision
Approximations of Shape Metrics and Application to Shape Warping and Empirical Shape Statistics
Foundations of Computational Mathematics
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 Based Shape Prior Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
International Journal of Computer Vision
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Segmentation of thin structures in volumetric medical images
IEEE Transactions on Image Processing
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We show a new segmentation technical method that takes shape and appearance data into account. It uses the level set technique. The algorithm merges the edge alignment and homogeneity terms with a shape dissimilarity measure in the segmentation task. Specifically, we make two contributions. In relation to appearance, we propose a new preprocessing step based on non-linear diffusion. The objective is to improve the edge detection and the region smoothing. The second and main contribution is an analytic formulation of the non-rigid transformation of the shape prior over the inertial center of the active contour. We have assumed gaussian density on the sample set of the shape prior and we have applied principal component analysis (PCA). Our method have been validated using 2D and 3D images, including medical images of the liver.