C.-F. Westin, A. Bhalerao, H. Knutsson et al. "Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images." In Proc. of Computer Vision and Pattern Recognition '97. Puerto Rico, 1997. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Estimating Local and Global Image Structure using a.. - Abhir Bhalerao And (2001) Self-citation (Bhalerao) (Correct)
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C.-F. Westin, A. Bhalerao, H. Knutsson et al. "Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images." In Proc. of Computer Vision and Pattern Recognition '97. Puerto Rico, 1997.
Estimating Local and Global Image Structure using a Gaussian .. - Bhalerao, Wilson (2001) Self-citation (Bhalerao) (Correct)
....within large data sets is desirable for image segmentation, matching and visualisation, where computational efficiency is important. Previous work in this area has ranged from modelling specific features using deformable templates (e. g for vasculature [1] filtering and adaptive thresholding [2], and using scale space approaches [3, 4] to recently reported work using level sets [5] Statistical methods for image analysis typically use likelihood techniques like Expectation Maximisation to model the global intensity variation, e.g. 6] from which a segmentation can be derived. Here, we ....
....associated with the smallest eigenvalue is the direction of the line; for planes the eigenvector associated with the largest eigenvalue is the normal to the plane. A measure of the local linearity, planarity or isotropy of the feature can be calculated by taking ratios of the eigenvalues [2], table 1. Together with 2 , these indices can be used to classify a block at any spatial size. Note that c p c l c s = 1. Interpretation Eigenvalues Index planes 1 2 3 cp = 1 2 1 1 lines 1 2 3 c l = 2 3 1 1 spheres 1 2 3 cs = 3 1 1 ....
C.-F. Westin, A. Bhalerao, H. Knutsson et al. "Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images." In Proc. of Computer Vision and Pattern Recognition '97. Puerto Rico, 1997.
High Performance Computing (HPC) in Medical Image.. - Kikinis, Warfield.. Self-citation (Kikinis) (Correct)
....and application. Idea Method Parallelization Feature Enhancement Enhance selected characteristics Spatial and frequency domain filtering: convolutions SMP and MPI style for Fourier transforms [Frigo, 1997]and convolutions Noise reduction [Gerig, 1992] removal of partial volume artefacts [Westin, 1997] Classification k NN, Parzen window Classify an unknown voxel based on prototypes Nonparametric supervised statistical classification ( Duda, 1973] Cover, 1967] Cover, 1968] Clarke, 1993] Warfield, 1996] Friedman, 1975] Each voxel treated separately ( Friedman, 1975] SMP for core, MPI ....
....the orbits. However, the signal intensities are reversed in the two example scenarios (joint spaces are dark and thin bone is bright in CT data) Accordingly, 3D reconstruction for craniofacial surgery will benefit from improved segmentation results. Here we use local 3D structure for segmentation [Westin, 1997]. A tensor descriptor is estimated for each neighborhood, for each voxel in the data set [Knutsson, 1989] The tensors are created from a combination of the outputs form a set of 3D quadrature filters. The shape of the tensors describe locally the structure of the neighborhood in terms of how much ....
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Westin C-F, A. Bhalerao, H. Knutsson and R. Kikinis. 1997. Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'97), San Juan, Puerto Rico.
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