Supervised Segmentation of Volume Textures Using 3D Probabilistic Relaxation
An iterative 3D probabilistic relaxation scheme has been developed for assigning labels to voxels based on the probabilities that the voxel belongs to each one of a number of known classes. The approach takes account of the probabilities of the neighbouring voxels belonging to each class and of the likely configurations of those labels within the neighbourhood. We apply the approach to the supervised segmentation of a seismic volume. In the example, the probability that a voxel belongs to each class is provided by the application of gradient operators and statistical measures. The iterative relaxation scheme then assigns the most appropriate label to each voxel.
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