Bayesian Level Sets for Image Segmentation

This paper presents a new general framework for image segmentation. A level set formulation is used to model the boundaries of the image regions and a new Multilabel Fast Marching is introduced for the evolution of the region contours toward the segmentation result. Statistical tests are performed to yield an initial estimate of high-confidence subsets of the image regions. Furthermore, the velocities for the propagation of the region contours are defined in accordance with the a posteriori probability of the respective regions, leading to the Bayesian Level Set methodology described in this paper. Typical segmentation problems are considered and experimental results are given to illustrate the robustness of the method against noise and its performance in precise region boundary localization.

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