Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Multiscale detection of curvilinear structures in 2-D and 3-D image data
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
International Journal of Computer Vision
Gradient competition anisotropy for centerline extraction and segmentation of spinal cords
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Inspired by the motion of a solid surface under liquid pressure, this paper proposes a novel deformable surface model to segment blood vessels in medical images. In the proposed model, the segmented region and the background region are respectively considered as liquid and an elastic solid. The surface of the elastic solid experiences various forces derived from the second order intensity statistics and the surface geometry. These forces cause the solid surface to deform in order to segment vascular structures in an image. The proposed model has been studied in the experiments on synthetic data and clinical data acquired by different imaging modalities. It is experimentally shown that the new model is robust to intensity contrast changes inside blood vessels and thus very suitable to perform vascular segmentation.