A novel method of incorporating shape information into the image segmentation process is presented We introduce a representation for deformable shapes and defrne a pmbabiliiy distribution over lhe variances of a set of training shapes. The segmentationpmcess embeds an initial curve as the zem level set of a higher dimensional surface, and evolves the surface such that the zem level set converges on the boundary of the object to be segmented At each step of the surface evolutio~ we estimate the "wn aposteriori (MAP) position and shape of the object in the image, based on the prior shape informah'on and the image information. We then evolve the surface globally, towardr the MAP estimate, and locally, based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery, in 2 0 and3D.
The Leventon et al. REF formulation of the geodesic active contours method uses an additional shape-influenced term in the driving PDE using a maximum a posteriori (MAP) estimate of the position and shape of the object in the image.
Statistical shape influence in geodesic active contours
5th IEEE EMBS International Summer School on Biomedical Imaging, 2002.
5th IEEE EMBS International Summer School on Biomedical Imaging, 2002.
Mathematics