An adaptive filtering method for volume data is presented in this paper. In this filtering method, the input data set is re-sampled to create a hierarchy of multiple-level data sets. A data classification task is performed at each level of the data pyramid to decide the local structure types. Data voxels are classified as linear, planar, or blob structures, based on the gradients and the eigenvalues of Hessian matrices. The classification results are used to adjust the shapes and orientations of filters such that noises are suppressed while key features are preserved.
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