Co-dimension 2 Geodesic Active Contours for MRA Segmentation
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
A new model for the recovery of cylindrical structures from medical image data
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
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Model based segmentation for retinal fundus images
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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We describe a method for inferring vascular (tree-like) structures from 2D and 3D imagery. A Bayesian formulation is used to make effective use of prior knowledge of likely tree structures with the observed being modelled locally with intensity profiles as being Gaussian. The local feature models are estimated by combination of a multiresolution, windowed Fourier approach followed by an iterative, minimum mean-square estimation, which is both computationally efficient and robust. A Markov Chain Monte Carlo (MCMC)algorit hm is employed to produce approximate samples from the posterior distribution given the feature model estimates. We present results of the multiresolution parameter estimation on representative 2D and 3D data, and show preliminary results of our implementation of the MCMC algorithm.