Difference between revisions of "Projects:StatisticalSegmentationSlicer2"
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| − | Back to | + | Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]] |
| − | + | __NOTOC__ | |
= Statistical Segmentation Slicer 2 = | = Statistical Segmentation Slicer 2 = | ||
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* 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] | ||
| − | = | + | = Improvements = |
| + | |||
| + | Improvements over the original method are [[RobustStatisticsSegmentation|here.]] | ||
| − | + | = Key Investigators = | |
| − | + | * Georgia Tech Algorithms: Delphine Nain, Eric Pichon, Oleg Michailovich, Yogesh Rathi, James Malcolm, Allen Tannenbaum | |
| − | + | = Publications = | |
| − | = | + | ''In print'' |
| + | * [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] | ||
| − | + | Note: Best student presentation in image segmentation award[http://www.bme.gatech.edu/groups/minerva/publications/papers/pichon-media2004-segmentation.pdf [1]] | |
| − | + | [[Category: Slicer]] [[Category: Segmentation]] [[Category:Statistics]] | |
Latest revision as of 00:57, 16 November 2013
Home < Projects:StatisticalSegmentationSlicer2Back 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
- 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]
