Segmentation Of Brain Mr Images Using J-Divergence Based Active Contour Models
In this chapter we propose a novel variational formulation for brain MRI segmentation. The originality of our approach is on the use of J-divergence (symmetrized Kullback-Leibler divergence) to measure the dissimilarity between local and global regions. In addition, a three-phase model is proposed to perform the segmentation task. The voxel intensity value of all regions is assumed to follow Gaussian distribution. It is introduced to ensure the robustness of the algorithm when an image is corrupted by noise. J-divergence is then used to measure the “distance” between the local and global region probability density functions. The proposed method yields promising results on synthetic and real brain MR images.
KeywordsActive Contour Active Contour Model Signed Distance Function Geometric Active Contour Model Geodesic Active Region
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