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Home < ProjectWeek200706:vtkITKWrapperForRuleBasedSegmentation
Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.

Key Investigators

  • Georgia Tech: John Melonakos, Ramsey Al-Hakim
  • Kitware: Brad Davis
  • BWH: Marek Kubicki
  • UCI: Jim Fallon


We are developing rule-based segmentation techniques which speedup the process and improve the accuracy for delineating the DLPFC in brain MRI scans. Our objective is to develop Slicer modules to facilitate clinical use of these techniques.

A functional Slicer2 module has been developed and now needs to be tested and used by our Core 3 partners. Furthermore, in order to provide continued support for this module, we will port this code to Slicer3.

Approaches and Challenges

Our approach for segmenting the DLPFC is described in the references below. The challenge is to make this software user-friendly to enable clinical use of the tool.


June 2007 Project Week

This is where you put in progress made in Project Week 2007.


This code was developed between 2005-2007. First is was developed and tested in Matlab. Then the sub-volume creation rules were ported to Slicer2 while the Bayesian segmentation was ported to ITK (see the references below for more detail). Finally, in early 2007, a vtk wrapper of the ITK Bayesian code was developed, thus completing the Slicer2 RuleBasedSegmentation module.


  • Ramsey Al-Hakim, James Fallon, Delphine Nain, John Melonakos, and Allen Tannenbaum. A dorsolateral prefrontal cortex semi-automatic segmenter. In SPIE Medical Imaging, 2006.
  • J. Melonakos, K. Krishnan, and A. Tannenbaum. An ITK Filter for Bayesian Segmentation: itkBayesianClassifierImageFilter. Insight Journal, 2006.
  • J. Melonakos, R. Al-Hakim, J. Fallon, and A. Tannenbaum. Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit. Insight Journal, 2005.