Level Set Segmentation with Both Shape and Intensity Priors

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 of the shape has a highly inhomogeneous intensity distribution. We demonstrate our segmentation algorithm using challenging pelvis CT scans.