Supervised Segmentation of Volume Textures Using 3D Probabilistic Relaxation

  • Matthew Deighton
  • Maria Petrou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

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.

References

  1. 1.
    N. Nikolaidis and I. Pitas, 3-D Image Processing Algorithms, John Wiley and Sons, 2001.Google Scholar
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    V. Kovalev, M. Petrou and Y. Bondar, “Texture anisotropy in 3-D images”, IEEE Transactions on Image Processing, Vol. 8, No 3, Pages 346–360, 1999.CrossRefGoogle Scholar
  3. 3.
    M. Mirmehdi and M. Petrou, “Segmentation of colour textures” IEEE Trans. on Pattern Analysis and Machine Intelligence, pages 142–159, Vol. PAMI-22, No. 2, 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Matthew Deighton
    • 1
  • Maria Petrou
    • 1
    • 2
  1. 1.School of Electronics and Physical SciencesUniversity of SurreyGuildfordUK
  2. 2.EKETAThe Intstitute of Telematics and InformaticsThermi, ThessalonikiGreece

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