Difference between revisions of "NA"

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== Testing Methods for Statistical Shape Analysis ==
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'''Objective:''' Recently methods and algorithms have been developed to trace white matter fibers in a stochastic matter. We want to use one of these methods on datasets with both f-MRI and Difussion weighted images. The goal is to get a probability of connection between f-MRI activated ROI's, and see if there is a difference in schizophrenic and control groups.
  
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=== Description ===
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'''Progress:''' We have code running in matlab which was used to give prove of concept of our method, which was developed by Ola Friman. We made some improvements to his implemenation, and it is now possible to load nrrd data sets and export the paths to vtk. Currently we are processing some of our datasets, and are able to create probability maps between ROI's.
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3 Main correction methods in local statistical shape analysis for the multiple comparison problem exists.
 
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# Bonferroni correction (simple, worst case, conservative)
 
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# Minimum statistic using Non-parametric permutation tests (less conservative than Bonferroni, full control of family wise error rate (FWER, Type I error, false positives), still pessimistic)
 
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# False Discovery Rate (FDR, control of false discovery rate, i.e. control of false positive in only those features that reject Null Hypothesis, less conservative)
 
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Currently the UNC shape analysis pipeline incorporates the permutation test based correction technique. This project will
 
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* extend the shape analysis pipeline to incorporate Bonferroni and FDR
 
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* change the current code to use the ITK statistics framework
 
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=== Current Status ===
 
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* permutation tests are working
 
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* both bonferroni and FDR will be rather simple to implement, the bulk of work will be associated with transfering the code into the ITK framework
 
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* [[Image:2006_06_PW_StatAnal.ppt|Image:2006 06 PW StatAnal.ppt]]
 
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=== Members ===
 
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* Martin Styner (UNC)
 

Revision as of 14:03, 18 December 2006

Home < NA

Objective: Recently methods and algorithms have been developed to trace white matter fibers in a stochastic matter. We want to use one of these methods on datasets with both f-MRI and Difussion weighted images. The goal is to get a probability of connection between f-MRI activated ROI's, and see if there is a difference in schizophrenic and control groups.

Progress: We have code running in matlab which was used to give prove of concept of our method, which was developed by Ola Friman. We made some improvements to his implemenation, and it is now possible to load nrrd data sets and export the paths to vtk. Currently we are processing some of our datasets, and are able to create probability maps between ROI's.

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