Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
3D object segmentation using B-Surface
Image and Vision Computing
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In this paper a modular approach of segmentation which combines the Bayesian model with the deformable model is proposed. It is based on the level set method, and breaks up into two great parts. Initially, a preliminary stage allows constructing the information map. Then, a deformable model, implemented with the Generalized Fast Marching Method (GFMM), evolves towards the structure to be segmented, under the action of a force defined from the information map. This last is constructed from the posterior probability information. The major contribution of this work is the use and the improvement of the GFMM for the segmentation of 3D images and also the design of a robust evolution model based on adaptive parameters depending on the image. Experimental evaluation of our segmentation approach on several MRI volumes shows satisfactory results.