Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Multiscale Detection of 3D Vessels
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Nonparametric robust methods for computer vision
Nonparametric robust methods for computer vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A common viewpoint on broad kernel filtering and nonlinear diffusion
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Smoothed local histogram filters
ACM SIGGRAPH 2010 papers
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In this paper we investigate the ability of the mean shift (MS) algorithm for denoising of 3D Computer Tomography (CT) data sets. The large size of the volume data sets makes it infeasible to apply a 3D version of the MS algorithm directly. Therefore, we introduce a variant of the MS algorithm using information propagation. We would like to make use of the 3D nature of the data with a considerably reduced running time of the algorithm. The proposed version is compared to a 2D implementation of the same algorithm applied slice by slice and other filter methods such as median filter and bilateral filtering. The advantages and disadvantages of each algorithm are shown on different CT data sets.