Difference between revisions of "2012 Winter Project Week:TBIRegistration"
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<h3>Objective</h3> | <h3>Objective</h3> | ||
| − | * Design a registration algorithm that can deal with topological changes for TBI patients. | + | * A short version of conference submission. |
| − | * Design metrics to quantify the degree of changes of TBI | + | * 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 | ||
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<h3>Approach, Plan</h3> | <h3>Approach, Plan</h3> | ||
| − | + | * 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 | ||
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<h3>Progress</h3> | <h3>Progress</h3> | ||
| + | * 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 | ||
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)
- Slicer Module
- 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
