We present a new variational level-set-based segmentation
formulation that uses both shape and intensity prior information
learned from a training set. By applying Bayes’
rule to the segmentation problem, the cost function decomposes
into shape and image energy parts. The shape energy
is based on recently proposed nonparametric shape
distributions, and we propose a new image energy model
that incorporates learned intensity information from both
foreground and background objects. The proposed variational
level set segmentation framework has two main advantages.
First, by characterizing image information with
regional intensity distributions, there is no need to balance
image energy and shape energy using a heuristic weighting
factor. Second, by incorporating learned intensity information
into the image model using a nonparametric density estimation
method and an appropriate distance measure, our
segmentation framework can handle problems where the interior/
exterior o...
Siqi Chen and Richard J. Radke