Entropic Estimation of Noise for Medical Volume Restoration

  • Authors:

  • Marc Liévin;Franck Luthon;Erwin Keeve

  • Affiliations:

  • -;-;-

  • Venue:
  • ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
  • Year:
  • 2002

  • Fuzzy Classification of Restored MRI Images

    Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence

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Abstract

This paper presents an unsupervised approach for medical volume restoration. To cope with various scanning modalities and strongly corrupted data, an original information tool is introduced: the entropic deviation. To validate the robustness of this estimation, a non-linear restoration filter based on Markov random fields is proposed. No parameter tuning is required from the user thanks to the adaptive value of the entropy power. Finally, the good quality of the filtered volumes are promising for any clustering application aiming at anatomical structures extraction in medical volume datasets.