Statistical shape influence in geodesic active contours

TLDR

The study introduces a novel shape‑informed segmentation method that represents deformable shapes probabilistically to guide image segmentation. The method embeds an initial curve as the zero level set of a higher‑dimensional surface, then iteratively evolves the surface toward the MAP estimate of shape and position using prior shape statistics and image gradients, converging on object boundaries. Experiments on synthetic and medical images in 2D and 3D demonstrate the method’s effectiveness.

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 min 3D.