Detail publikace

Tetrahedral Meshing of Volumetric Medical Images Respecting Image Edges

Originální název

Tetrahedral Meshing of Volumetric Medical Images Respecting Image Edges

Anglický název

Tetrahedral Meshing of Volumetric Medical Images Respecting Image Edges

Jazyk

en

Originální abstrakt

In this paper, a modified variational tetrahedral meshing approach is used to adapt a tetrahedral mesh to the underlying CT volumetric data so that image edges are well approximated in the mesh. Finally, tetrahedra in the mesh are classified into regions whose image characteristics are similar. Three different clustering schemes are proposed to classify tetrahedra, while the clustering scheme viewing the mesh as an undirected graph with edges weighted according to similarity of tetrahedra achieved best results.

Anglický abstrakt

In this paper, a modified variational tetrahedral meshing approach is used to adapt a tetrahedral mesh to the underlying CT volumetric data so that image edges are well approximated in the mesh. Finally, tetrahedra in the mesh are classified into regions whose image characteristics are similar. Three different clustering schemes are proposed to classify tetrahedra, while the clustering scheme viewing the mesh as an undirected graph with edges weighted according to similarity of tetrahedra achieved best results.

BibTex


@inproceedings{BUT76362,
  author="Michal {Španěl} and Přemysl {Kršek} and Miroslav {Švub} and Vít {Štancl}",
  title="Tetrahedral Meshing of Volumetric Medical Images Respecting Image Edges",
  annote="In this paper, a modified variational tetrahedral meshing approach is used to
adapt a tetrahedral mesh to the underlying CT volumetric data so that image edges
are well approximated in the mesh. Finally, tetrahedra in the mesh are classified
into regions whose image characteristics are similar. Three different clustering
schemes are proposed to classify tetrahedra, while the clustering scheme viewing
the mesh as an undirected graph with edges weighted according to similarity of
tetrahedra achieved best results.",
  address="Springer Verlag",
  booktitle="Proceedings of the 14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011",
  chapter="76362",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  year="2011",
  month="august",
  pages="161--169",
  publisher="Springer Verlag",
  type="conference paper"
}