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Since both the EM model fitting algorithm and the ICM labelling
algorithm converge locally, the choice of initial conditions,
including the initial parameter set and the classification, is
important.
Without prior information, histogram analysis is widely used to
estimate statistics such as means and variances of a distribution.
Histogram fitting is the most commonly adopted method, especially
for the Gaussian mixture model [29]. But in the case
when the image has high noise level, the Gaussian mixture
assumption may not hold and fitting may fail. From the standpoint
of classification, we want the classes to be widely separated from
each other while at the same time having relatively low
intra-class variances. Whether the intensity PDF follows a
Gaussian mixture or not is relatively unimportant. According to
this, we adopt an initial estimation in this paper a discriminant
measure-based thresholding method proposed by Otsu [25].
The basic idea is to find thresholds maximizing the inter-class
variances thus also minimizing the intra-class variances.
According to theories of discriminant analysis, such thresholds
are optimal solutions. Once the optimal thresholds have been
determined, the mean
and the standard deviation
for
each class type can then be used as the initial parameters for
further estimation. The initial classification can also be
obtained either directly through the thresholding, or through an
ML estimation with those known parameters.
Next: Experiments
Up: Segmentation Using the HMRF-EM
Previous: Segmentation Using the HMRF-EM
Yongyue Zhang
2000-05-11