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Fast asymmetric fronts propagation for voronoi region partitioning and image segmentation

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Abstract

In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations. One of the key ingredients for the computation of the geodesic distance map is the geodesic metric, which can govern the action of the geodesic distance level set propagation. We consider a Finsler metric with the Randers form, through which the asymmetry and anisotropy enhancements can be taken into account to prevent the fronts leaking problem during the fronts propagation. These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow. The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes.

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Chen, D., & Cohen, L. D. (2018). Fast asymmetric fronts propagation for voronoi region partitioning and image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10746 LNCS, pp. 469–484). Springer Verlag. https://doi.org/10.1007/978-3-319-78199-0_31

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