Model-based detection of tubular structures in 3D images
Computer Vision and Image Understanding
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
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
MMBIA '00 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
A Real-Time Algorithm for Medical Shape Recovery
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Vesselness enhancement diffusion
Pattern Recognition Letters
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Geometric Deformable Model Driven by CoCRFs: Application to Optical Coherence Tomography
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
CoCRF deformable model: a geometric model driven by collaborative conditional random fields
IEEE Transactions on Image Processing
Probabilistic branching node detection using hybrid local features
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Bayesian tracking of elongated structures in 3D images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Bayesian tracking of tubular structures and its application to carotid arteries in CTA
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Probabilistic branching node detection using adaboost and hybrid local features
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Deformable probability maps: Probabilistic shape and appearance-based object segmentation
Computer Vision and Image Understanding
Segmentation for pulmonary artery from mediastinum to lung in CTA datasets
International Journal of Innovative Computing and Applications
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In this paper we propose a Particle Filter-based propagation approach for the segmentation of vascular structures in 3D volumes. Because of pathologies and inhomogeneities, many deterministic methods fail to segment certain types of vessel. Statistical methods represent the solution using a probability density function (pdf). This pdf does not only indicate the best possible solution, but also valuable information about the solution's variance. Particle Filters are used to learn the variations of direction and appearance of the vessel as the segmentation goes. These variations are used in turn in the particle filters framework to control the perturbations introduced in the Sampling Importance Resampling step (SIR). For the segmentation itself, successive planes of the vessel are modeled as states of a Particle Filter. Such states consist of the orientation, position and appearance (in statistical terms) of the vessel. The shape of the vessel and subsequently the particles pdf are recovered using globally active contours, implemented using circular shortest paths by branch and bound [1] that guarantees the global optimal solution. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.