Difference between revisions of "Projects:ShapeBasedLevelSetSegmentation"

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= Shape Based Level Set Segmentation =
 
This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection.
 
This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection.
  
= Descriptions =
 
 
''Software''
 
 
Already in ITK.
 
Already in ITK.
 
= Key Investigators =
 
  
 
= Publications =
 
= Publications =
  
Michael Leventon, Eric Grimson, Olivier Faugeras. "Statistical Shape Influence in Geodesic Active Contours" Comp. Vision and Patt. Recon. (CVPR), 2000.
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''In Print''
  
Michael Leventon, Olivier Faugeras, Eric Grimson, William Wells. "Level Set Based Segmentation with Intensity and Curvature Priors" Mathematical Methods in Biomedical Image Analysis. (MMBIA), 2000.
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* [http://www.na-mic.org/publications/pages/display?search=Projects%3AShapeBasedLevelSetSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Shape Based Level Segmentation]
  
= Links =
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[[Category: Segmentation]]

Latest revision as of 19:59, 11 May 2010

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Shape Based Level Set Segmentation

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection.

Already in ITK.

Publications

In Print