Abstract
Given an input 3D image, in this paper we first segment it into several clusters by extending the 2D harmonic edge-weighted centroidal Voronoi tessellation (HEWCVT) method to the 3D image domain. The Dual Contouring method is then applied to construct tetrahedral meshes by analyzing both material change edges and interior edges. An anisotropic Giaquinta-Hildebrandt operator (GHO) based geometric flow method is developed to smooth the surface with both volume and surface features preserved. Optimization based smoothing and topological optimizations are also applied to improve the quality of tetrahedral meshes. We have verified our algorithms by applying them to several datasets.
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CITATION STYLE
Hu, K., Zhang, Y. J., & Xu, G. (2017). CVT-based 3D image segmentation for quality tetrahedral meshing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10149 LNCS, pp. 27–42). Springer Verlag. https://doi.org/10.1007/978-3-319-54609-4_2
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