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  Back to [[NA-MIC_Collaborations|NA-MIC_Collaborations]]
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  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
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__NOTOC__
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= Statistical Segmentation Slicer 2 =
  
'''Objective:'''
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Our objective is 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.
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= Description =
 
 
'''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
  
<div class="thumb tright"><div style="width: 182px">[[Image:Gatech_SlicerModel2.jpg|[[Image:180px-Gatech_SlicerModel2.jpg|Figure 1:Screenshot from the Slicer Fast Marching module]]]]<div class="thumbcaption"><div class="magnify" style="float: right">[[Image:Gatech_SlicerModel2.jpg|[[Image:magnify-clip.png|Enlarge]]]]</div>Figure 1:Screenshot from the Slicer Fast Marching module</div></div></div>
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[[Image:Gatech_SlicerModel2.jpg|thumb|right|180px|Figure 1:Screenshot from the Slicer Fast Marching module]]
  
 
* 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]
  
'''Key Investigators:'''
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= Improvements =
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Improvements over the original method are [[RobustStatisticsSegmentation|here.]]
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= Key Investigators =
  
Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum
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* Georgia Tech Algorithms: Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum
  
''References:''
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= Publications =
  
* 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]]
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''In print''
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* [http://www.na-mic.org/publications/pages/display?search=StatisticalSegmentationSlicer2&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&searchbytag=checked&sponsors=checked| NA-MIC Publications Database on Statistical/PDE Methods using Fast Marching for Segmentation]
  
'''Links:'''
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Note: Best student presentation in image segmentation award[http://www.bme.gatech.edu/groups/minerva/publications/papers/pichon-media2004-segmentation.pdf [1]]
  
* [[Algorithm:GATech|Georgia Tech Algorithms]]
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[[Category: Slicer]] [[Category: Segmentation]] [[Category:Statistics]]
* [[NA-MIC_Collaborations|NA-MIC Collaborations]]
 

Latest revision as of 00:57, 16 November 2013

Home < Projects:StatisticalSegmentationSlicer2
Back to Stony Brook University Algorithms

Statistical Segmentation Slicer 2

Our objective is 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.

Description

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

Improvements

Improvements over the original method are here.

Key Investigators

  • Georgia Tech Algorithms: Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum

Publications

In print

Note: Best student presentation in image segmentation award[1]