Difference between revisions of "Projects:ShapeBasedLevelSetSegmentation"
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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. | ||
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''Software'' | ''Software'' | ||
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Already in ITK. | Already in ITK. | ||
Revision as of 01:09, 21 September 2007
Home < Projects:ShapeBasedLevelSetSegmentationBack to NA-MIC_Collaborations, MIT Algorithms
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.
Key Investigators
Publications
Michael Leventon, Eric Grimson, Olivier Faugeras. "Statistical Shape Influence in Geodesic Active Contours" Comp. Vision and Patt. Recon. (CVPR), 2000.
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.
