Statistical shape influence in geodesic active contours

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Statistical shape influence in geodesic active contours (2000)

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by Michael E. Leventon , W. Eric , L. Grimson , Olivier Faugeras

Venue:	In Proc. 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC
Citations:	396 - 4 self

BibTeX

@INPROCEEDINGS{Leventon00statisticalshape,
    author = {Michael E. Leventon and W. Eric and L. Grimson and Olivier Faugeras},
    title = {Statistical shape influence in geodesic active contours},
    booktitle = {In Proc. 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC},
    year = {2000},
    pages = {316--323}
}

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Abstract

A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior shape information and the image information. We then evolve the surface globally, towards the MAP estimate, and locally, based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery, in 2D and 3D. 1

Keyphrases

statistical shape influence geodesic active contour initial curve training shape synthetic data image gradient surface evolution image segmentation process level set shape information probability distribution prior shape information segmentation process map estimate image information dimensional surface deformable shape medical imagery novel method

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