### 抄録

Cross-sectioning is a popular method for visualizing the complicated inner structures of three-dimensional volume datasets. However, the process is usually manual, meaning that a user must manually specify the cross-section's location using a repeated trial-and-error process. To find the best cross-sections, this method requires that a user is knowledgeable and experienced. This paper proposes a method for automatically generating characteristic cross-sections from a given volume dataset. The application of a volume skeleton tree (VST), which is a graph that delineates the topological structure of a three-dimensional volume, facilitates the automated generation of cross-sections giving good representations of the topological characteristics of a dataset. The feasibility of the proposed method is demonstrated using several examples.

元の言語 | English |
---|---|

ホスト出版物のタイトル | Lecture Notes in Computer Science |

編集者 | A. Butz, B. Fisher, A. Kruger, P. Olivier |

ページ | 175-184 |

ページ数 | 10 |

巻 | 3638 |

出版物ステータス | Published - 2005 |

外部発表 | Yes |

イベント | 5th International Symposium on Smart Graphics, SG 2005 - Frauenworth Cloister, Germany 継続期間: 2005 8 22 → 2005 8 24 |

### Other

Other | 5th International Symposium on Smart Graphics, SG 2005 |
---|---|

国 | Germany |

市 | Frauenworth Cloister |

期間 | 05/8/22 → 05/8/24 |

### ASJC Scopus subject areas

- Computer Science (miscellaneous)

### これを引用

*Lecture Notes in Computer Science*(巻 3638, pp. 175-184)

**Automatic cross-sectioning based on topological volume skeletonization.** / Mori, Yuki; Takahashi, Shigeo; Igarashi, Takeo; Takeshima, Yuriko; Fujishiro, Issei.

研究成果: Conference contribution

*Lecture Notes in Computer Science.*巻. 3638, pp. 175-184, 5th International Symposium on Smart Graphics, SG 2005, Frauenworth Cloister, Germany, 05/8/22.

}

TY - GEN

T1 - Automatic cross-sectioning based on topological volume skeletonization

AU - Mori, Yuki

AU - Takahashi, Shigeo

AU - Igarashi, Takeo

AU - Takeshima, Yuriko

AU - Fujishiro, Issei

PY - 2005

Y1 - 2005

N2 - Cross-sectioning is a popular method for visualizing the complicated inner structures of three-dimensional volume datasets. However, the process is usually manual, meaning that a user must manually specify the cross-section's location using a repeated trial-and-error process. To find the best cross-sections, this method requires that a user is knowledgeable and experienced. This paper proposes a method for automatically generating characteristic cross-sections from a given volume dataset. The application of a volume skeleton tree (VST), which is a graph that delineates the topological structure of a three-dimensional volume, facilitates the automated generation of cross-sections giving good representations of the topological characteristics of a dataset. The feasibility of the proposed method is demonstrated using several examples.

AB - Cross-sectioning is a popular method for visualizing the complicated inner structures of three-dimensional volume datasets. However, the process is usually manual, meaning that a user must manually specify the cross-section's location using a repeated trial-and-error process. To find the best cross-sections, this method requires that a user is knowledgeable and experienced. This paper proposes a method for automatically generating characteristic cross-sections from a given volume dataset. The application of a volume skeleton tree (VST), which is a graph that delineates the topological structure of a three-dimensional volume, facilitates the automated generation of cross-sections giving good representations of the topological characteristics of a dataset. The feasibility of the proposed method is demonstrated using several examples.

UR - http://www.scopus.com/inward/record.url?scp=26944463432&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=26944463432&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:26944463432

VL - 3638

SP - 175

EP - 184

BT - Lecture Notes in Computer Science

A2 - Butz, A.

A2 - Fisher, B.

A2 - Kruger, A.

A2 - Olivier, P.

ER -