[Insight-users] itkExpectationMaximizationMixtureModelEstimator

[Christoph Heinzl]

Hi

In my recent application I need to fit gaussian normal distributions
into the histograms of my volumetric CT datasets. Usually there are
two or three different classes in my data: air regions, material1,
material2. So, first of all I would like to identify the means of my
clusters, which is working fine with K-means algorithm. In order to
fit now the normal distributions, I some kind of expectation algorithm
should work fine. As I saw in the ITK book and from prior postings
there is the itkExpectationMaximizationMixtureModelEstimator which
should be able to find both the means of the clusters and the variance
of these distributions. But my question is: How should the input data
for this funtion look like? If I input an array containing my
histogram data (from bin 0 to 65535) I don't get a valuable result.
The algorithm stops at the 1st iteration returning my preset initial
values for the clusters. Should the input data be presorted somehow?
Is the raw data of the dataset expected als input or the cumulated
histogram information?

THX in advance,
Chris