[Insight-users] Bayesian Classifier Initialization filter

[Aditya Chandramouli]

Hi,

I'm working on a project that requires a quick initial segmentation on brain 
tissue which will then be refined.

To do this initial segmentation, I've tried the KMeans classification 
algorithms in the Statistics package (scalar image as well as the kd-tree 
one). Unfortunately both run very slow on 3D images. The tree-based algorithm 
takes a very long time to generate the tree (in the order of a few minutes) 
whereas the equivalent code in another toolkit (FSL) takes just a few 
seconds.

I also tried the new BayesianClassifierInitializationImageFilter which is just 
as slow. However, I would like to try out this filter using the Euclidian 
distance as membership functions instead of the defaults(Gaussian density 
functions) to see if it runs any faster.

Unfortunately, using "custom" membership functions for this filter is not yet 
documented in the example code and I've not been able to figure it out on my 
own so far.

Any help and advice would be most appreciated.

thanks

-aditya