Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Minimal Paths in 3D Images and Application to Virtual Endoscopy
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Vessel Axis Determination Using Wave Front Propagation Analysis
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
An Adaptive Minimal Path Generation Technique for Vessel Tracking in CTA/CE-MRA Volume Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Level Set Based Segmentation with Intensity and Curvature Priors
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
International Journal of Computer Vision
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
3D Multi-branch Tubular Surface and Centerline Extraction with 4D Iterative Key Points
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
IEEE Transactions on Image Processing
Geodesic voting for the automatic extraction of tree structures. Methods and applications
Computer Vision and Image Understanding
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This paper presents a geodesic voting method to segment tree structures, such as retinal or cardiac blood vessels. Many authors have used minimal cost paths, or similarly geodesics relative to a weight potential P, to find a vessel between two end points. Our goal focuses on the use of a set of such geodesic paths for finding a tubular tree structures, using minimal interaction. This work adapts the geodesic voting method that we have introduced for the segmentation of thin tree structures to the segmentation of tubular trees. The original approach of geodesic voting consists in computing geodesics from a set of end points scattered in the image to a given source point. The target structure corresponds to image points with a high geodesic density. Since the potential takes low values on the tree structure, geodesics will locate preferably on this structure and thus the geodesic density should be high. Geodesic voting method gives a good approximation of the localization of the tree branches, but it does not allow to extract the tubular aspect of the tree. Here, we use the geodesic voting method to build a shape prior to constrain the level set evolution in order to segment the boundary of the tubular structure. We show results of the segmentation with this approach on 2D angiogram images and 3D simulated data.