Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit

Please use this identifier to cite or link to this publication: http://hdl.handle.net/1926/44
An Insight Toolkit (ITK) implementation of our knowledgebased
segmentation algorithm applied to brain MRI scans is presented
in this paper. Our algorithm is a refinement of the work of Teo, Saprio,
and Wandall. The basic idea is to incorporate prior knowledge into the
segmentation through Bayesrule. Image noise is removed via an affine
invariant anisotropic smoothing of the posteriors as in Haker et. al.
We present the results of this code on two different projects. First, we
show the effect of applying this code to skull-removed brain MRI scans.
Second, we show the effect of applying this code to the extraction of the
DLPFC from a user-defined subregion of brain MRI data.We present our
results on brain MRI scans, comparing the results of the knowledge-based
segmentation to manual segmentations on datasets of schizophrenic patients.
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plus Statistical segmentation by Gavin Baker on 09-16-2005 for revision #1
starstarstarstarstar expertise: 3 sensitivity: 4.5
plus MRI Brain Image Segmentation by Vincent Magnotta on 08-08-2005 for revision #1
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Keywords: Knowledge-Based Segmentation, Brain MRI Scans, DLPFC, Bayes' Rule
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