Segmentation and Classification of Brain Spect Images Using 3 D Markov Random Field and Density Mixture Estimations

  title={Segmentation and Classification of Brain Spect Images Using 3 D Markov Random Field and Density Mixture Estimations},
  author={Max Mignotte and Jean Meunier and J.-P. Soucy and Christian Janicki},
Thanks to its ability to yield functionally rather than anat omicallybased information, the SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. N vertheless, SPECT images are very noisy and consequently their int rpretation is difficult. In order to facilitate this visualiz ation, we propose an unsupervised 3D Markovian model allowing to segment a brain SPECT image into three classes, corresponding t o the three existing cerebral tissues, respectively… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 15 references

Signal processing

  • S. Banks
  • image processing and pattern recognition…
  • 1990
Highly Influential
7 Excerpts

Unsupervised Bayesia n segmentation using hidden markovian fields

  • F. Salzenstein, W. Pieczinsky
  • In proc. International Conference on Acoustics…
  • 1995
2 Excerpts

Champs de Markov cachés et estimation conditionnelle itérative.Revue Traitement Du Signal

  • W. Pieczynski
  • 1994
2 Excerpts

Geman . Stochastic relaxation , Gibbs distributions and the Bayesian restoration of images

  • D.
  • IEEE Trans . on Pattern Analysis and Machine…
  • 1993

Global and loc al methods of unsupervised Bayesian segmentation of images

  • B. Braathen, P. Masson, W. Pieczynski
  • GRAPHICS and VISION , 2(1) :39–52
  • 1993
1 Excerpt

Brain Blood Flow in Neurology and Psychiatry

  • D. C. Costa, P. J. Ell
  • Series Editor : P.J. Ell
  • 1991
1 Excerpt

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