Difference between revisions of "2012 Winter Project Week:TBIRegistration"

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<h3>Objective</h3>
 
<h3>Objective</h3>
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* We are developing methods for segmenting the endocardium from the DE-MRI. The current proposed method is a multi-atlas based registration approach.  The user is presented with a weighted average of all registered atlas segmentations.  The final LA segmentation is a thresholded volume from this weighted average.  Weighting is based on the accuracy of the registration, as determined by a mutual information metric.
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* A short version of conference submission.  
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* There is a prototype module developed by Yi that is currently in Slicer. We propose to expand and tweak this module to specifications determined by our experience using the algorithm on many real patient datasets.
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* A in-depth discussion on the similarity measures for TBI image registration as a journal submission.  
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* We will also discuss posssible collaborative paper ideas based on this work.
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* Design a registration algorithm that can deal with topological changes for TBI patients.
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* Design metrics to quantify the degree of changes of TBI
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<h3>Approach, Plan</h3>
 
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Approach to be filled here.
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* Analyze on different similarity measures, such as mutual information, Bhattacharyya Distance, J-R Divergence and cross correlation etc
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* Compare with some state-of-the-art registration methods, such as FSL, FNIRT, AIR etc
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* Use Dice coefficient as a quantitative metric to measure the quality of registration algorithms
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<h3>Progress</h3>
 
<h3>Progress</h3>
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The current segmentation module is in the Slicer extension manager.
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* We have tailored a 10-page paper for a conference submission
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* We have discussed a few feasible aspects to polish our paper as in Approach
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* We will submit a journal version shortly
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Latest revision as of 00:29, 6 February 2012

Home < 2012 Winter Project Week:TBIRegistration

Key Investigators

  • Georgia Tech: Yifei Lou and Patricio Vela
  • Boston University: Allen Tannenbaum
  • UCLA: Andrei Irimia, Micah C. Chambers, Jack Van Horn and Paul M. Vespa

Objective

  • A short version of conference submission.
  • A in-depth discussion on the similarity measures for TBI image registration as a journal submission.
  • Design a registration algorithm that can deal with topological changes for TBI patients.
  • Design metrics to quantify the degree of changes of TBI


Approach, Plan

  • Analyze on different similarity measures, such as mutual information, Bhattacharyya Distance, J-R Divergence and cross correlation etc
  • Compare with some state-of-the-art registration methods, such as FSL, FNIRT, AIR etc
  • Use Dice coefficient as a quantitative metric to measure the quality of registration algorithms


Progress

  • We have tailored a 10-page paper for a conference submission
  • We have discussed a few feasible aspects to polish our paper as in Approach
  • We will submit a journal version shortly


Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

  1. Slicer Module
    1. Extension -- commandline

References

1. Yifei Lou, Andrei Irimia, Patricio Vela, Allen Tannenbaum, Micah C. Chambers, Jack Van Horn and Paul M. Vespa. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units. Submitted to IEEE Trans. on Medical Imaging. 2011

2. Yifei Lou and Allen Tannenbaum. Multimodal Deformable Image Registration via the Bhattacharyya Distance. Submitted to IEEE Trans. Image Process. 2011

3. Yifei Lou, Xun Jia, Xuejun Gu and Allen Tannenbaum. A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance. Insight Journal, 2011. [1]

4. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548

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