As Luis mentioned you should take a look at the bias field correction application in InsightApplications. <br><br>Given that you are using a bayesian classifier, you already have a rough estimate of the tissue statistics (priors). The bias field correction application uses precisely these (in this specific case, it assumes a gaussian model, using the mean and standard deviation of each tissue class) to estimate the correcting polynomial. So the application should be well suited to your problem.<br>
<br><div class="gmail_quote">On Wed, Feb 11, 2009 at 5:05 AM, <span dir="ltr"><<a href="mailto:a.bongers@mediri.com">a.bongers@mediri.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hi,<br>
<br>
I am trying to use a Bayes Classifier to segment white ang gray brain matter from MRI images. Works O.K. so far, but I have the problem that many 3D images have varying gray-values e.g. in within the white matter. Thats probably due to RF inhomognieties from the coil or the like.<br>
The problem is that due to these slowly varying intensities the segmentation mis-calssifies GM and WM e.g. in the back of the brain.<br>
<br>
Has anybody a helpful idea to get better results ?<br>
I was thinking about hig-pass filtering the images to homogenize the intensities.<br>
Is there a high- or bandpass filter in itk ?<br>
<br>
Thanks a lot!<br>
<br>
André<br>
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</blockquote></div><br><br clear="all"><br>-- <br>Karthik Krishnan<br>R&D Engineer,<br>Kitware Inc.<br>Ph: 518 371 3971 x119<br>Fax: 518 371 3971<br>