Ellipse fitting by accumulating five-point fits
Pattern Recognition Letters
Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
Model-based detection of tubular structures in 3D images
Computer Vision and Image Understanding
Delaunay Triangulation in Three Dimensions
IEEE Computer Graphics and Applications
Vessel Extractio Techniques and Algorithms: A Survey
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Particle filters, a quasi-monte carlo solution for segmentation of coronaries
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Editorial: Medical image segmentation: Quo Vadis
Computer Methods and Programs in Biomedicine
Real-Time 3D Stereo Tracking and Localizing of Spherical Objects with the iCub Robotic Platform
Journal of Intelligent and Robotic Systems
Image segmentation for robots: fast self-adapting gaussian mixture model
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
A novel method for retinal vessel tracking using particle filters
Computers in Biology and Medicine
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In this paper a method to extract cerebral arterial segments from CT angiography (CTA) is proposed. The segmentation of cerebral arteries in CTA is a challenging task mainly due to bone contact and vein contamination. The proposed method considers a vessel segment as an ellipse travelling in three-dimensional (3D) space and segments it out by tracking the ellipse in spatial sequence. A particle filter is employed as the main framework for tracking and is equipped with adaptive properties to both bone contact and vein contamination. The proposed tracking method is evaluated by the experiments on both synthetic and actual data. A variety of vessels were synthesized to assess the sensitivity to the axis curvature change, obscure boundaries, and noise. The experimental results showed that the proposed method is also insensitive to parameter settings and requires less user intervention than the conventional vessel tracking methods, which proves its improved robustness.