"The development of aid`s systems for the medical diagnosis is not easy thing because of presence
of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other
different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is described in this article. A first phase consists
in estimating the density of probability of the data by the Parzen-Rozenblatt method. The classification
procedure is completely automatic and doesn`t make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an automatic manner by an operator of
mathematical morphology called Skeleton by Influence Zones detection (SKIZ). The problem of initialization
of the prototypes as well as their number is transformed in an optimization problem; in more the procedure is
adaptive since it takes in consideration the contextual information presents in every voxel by an adaptive
and robust non parametric model by the Markov Fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior Marginal)."
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