Difference between revisions of "Projects:StatisticalSegmentationSlicer2"

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= Statistical Segmentation Slicer 2 =
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  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
 
  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
  
'''Objective:'''
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'''Objective'''
  
 
We want to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE frameowrk.
 
We want to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE frameowrk.
  
'''Progress:'''
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'''Progress'''
  
 
We developped flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows.
 
We developped flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows.
  
'''Completed:'''
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''Completed''
  
 
* A statistically based flow for image segmentation, using Fast Marching
 
* A statistically based flow for image segmentation, using Fast Marching
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* The code has been integrated into the Slicer
 
* The code has been integrated into the Slicer
 
* A user-oriented tutorial for the Fast Marching algorithm is available at:[http://www.bme.gatech.edu/groups/minerva/publications/papers/pichon.slicer.fastMarching/index.html Slicer Module Tutorial]
 
* A user-oriented tutorial for the Fast Marching algorithm is available at:[http://www.bme.gatech.edu/groups/minerva/publications/papers/pichon.slicer.fastMarching/index.html Slicer Module Tutorial]
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''References''
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* Eric Pichon, Allen Tannenbaum, and Ron Kikinis. A statistically based surface evolution method for medical image segmentation: presentation and validation. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 2, pages 711-720, 2003. Note: Best student presentation in image segmentation award[http://www.bme.gatech.edu/groups/minerva/publications/papers/pichon-media2004-segmentation.pdf [1]]
  
 
'''Key Investigators:'''
 
'''Key Investigators:'''
  
Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum
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* GaTech: Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum
  
''References:''
 
  
* Eric Pichon, Allen Tannenbaum, and Ron Kikinis. A statistically based surface evolution method for medical image segmentation: presentation and validation. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 2, pages 711-720, 2003. Note: Best student presentation in image segmentation award[http://www.bme.gatech.edu/groups/minerva/publications/papers/pichon-media2004-segmentation.pdf [1]]
 
  
 
'''Links:'''
 
'''Links:'''

Revision as of 19:37, 3 September 2007

Home < Projects:StatisticalSegmentationSlicer2

Statistical Segmentation Slicer 2

Back to NA-MIC_Collaborations

Objective

We want to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE frameowrk.

Progress

We developped flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows.

Completed

  • A statistically based flow for image segmentation, using Fast Marching
Figure 1:Screenshot from the Slicer Fast Marching module
  • The code has been integrated into the Slicer
  • A user-oriented tutorial for the Fast Marching algorithm is available at:Slicer Module Tutorial

References

  • Eric Pichon, Allen Tannenbaum, and Ron Kikinis. A statistically based surface evolution method for medical image segmentation: presentation and validation. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 2, pages 711-720, 2003. Note: Best student presentation in image segmentation award[1]

Key Investigators:

  • GaTech: Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum


Links: