Uncertainty-Driven Non-parametric Knowledge-Based Segmentation: The Corpus Callosum Case

In this paper we propose a novel variational technique for the knowledge based segmentation of two dimensional objects. One of the elements of our approach is the use of higher order implicit polynomials to represent shapes. The most important contribution is the estimation of uncertainties on the registered shapes, which can be used with a variable bandwidth kernel-based non-parametric density estimation process to model prior knowledge about the object of interest. Such a non-linear model with uncertainty measures is integrated with an adaptive visual-driven data term that aims to separate the object of interest from the background. Promising results obtained for the segmentation of the corpus callosum in MR mid-sagittal brain slices demonstrate the potential of such a framework.