Segmentation by Adaptive Geodesic Active Contours
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Semi-Implicit Covolume Method in 3D Image Segmentation
SIAM Journal on Scientific Computing
A logic framework for active contours on multi-channel images
Journal of Visual Communication and Image Representation
A Variational Method for Geometric Regularization of Vascular Segmentation in Medical Images
IEEE Transactions on Image Processing
Segmentation of 3d tubular structures by a PDE-Based anisotropic diffusion model
MMCS'08 Proceedings of the 7th international conference on Mathematical Methods for Curves and Surfaces
Framelet-Based algorithm for segmentation of tubular structures
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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In this work we introduce the composed segmentation (C-segmentation), that is a priori composition of sources to obtain a single one segmentation result according to specific logic combinations. The approach and the segmentation model are general but we apply the C-segmentation technique to the challenging problem of segmenting tubular-like structures. The reconstruction is obtained by continuously deforming an initial distance function following the Partial Differential Equation (PDE)-based diffusion model derived from a minimal volume-like variational formulation. The gradient flow for this functional leads to a nonlinear curvature motion model. An anisotropic variant is provided which includes a diffusion tensor aimed to follow the tube geometry. Numerical examples demonstrate the ability of the proposed method to produce high quality 2D/3D segmentations of complex and eventually incomplete synthetic and real data.