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We have presented a fully automatic approach for the segmentation
of brain MR images. The method is based on a HMRF-EM framework,
which is a combination of the hidden Markov random field (HMRF)
model and the associated MRF-MAP estimation and the EM fitting
procedures. The HMRF model is proposed in this paper as a
substitute for the widely used FM model, which is considered as
sensitive to noise and therefore not robust. As a general method,
the HMRF-EM algorithm could be applied to many other image
segmentation problems.
We also show that the framework can easily be extended by
incorporating other techniques in order to improve its performance
on certain problems. As an example, we demonstrated how the bias
field correction algorithm by Guillemaud and Brady
[13] can be incorporated into this framework. As a
result, a three-dimensional fully automatic approach for brain MR
image segmentation is achieved and significant improvements have
been observed in terms of both the bias field estimation and the
tissue classification.
Yongyue Zhang
2000-05-11