### Abstract

Original language | English |
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Pages | 18-26 |

Number of pages | 9 |

Publication status | Published - 29 May 2013 |

Externally published | Yes |

Event | GRAPP 2013 IVAPP 2013 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Duration: 29 May 2013 → … |

### Conference

Conference | GRAPP 2013 IVAPP 2013 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications |
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Period | 29/5/13 → … |

### Fingerprint

### Cite this

*An efficient alternative to compute the genus of binary volume models*. 18-26. Paper presented at GRAPP 2013 IVAPP 2013 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications, .

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**An efficient alternative to compute the genus of binary volume models.** / Cruz-Matías, Irving; Ayala, Dolors.

Research output: Contribution to conference › Paper

TY - CONF

T1 - An efficient alternative to compute the genus of binary volume models

AU - Cruz-Matías, Irving

AU - Ayala, Dolors

PY - 2013/5/29

Y1 - 2013/5/29

N2 - In this paper we present a method to compute the Euler characteristic (X) and the genus of a volume dataset. It uses an alternative decomposition model to represent binary volume datasets: the Compact Union of Disjoint Boxes (CUDB). The method is derived from the classical method used with a voxel model and the computation of x and the genus is achieved by analyzing the connectivity among boxes and using a CUDB connected-component labeling process. We have tested our method both with phantom and real datasets and we show that it is more efficient than previous methods based on the voxel model, and other alternative models.

AB - In this paper we present a method to compute the Euler characteristic (X) and the genus of a volume dataset. It uses an alternative decomposition model to represent binary volume datasets: the Compact Union of Disjoint Boxes (CUDB). The method is derived from the classical method used with a voxel model and the computation of x and the genus is achieved by analyzing the connectivity among boxes and using a CUDB connected-component labeling process. We have tested our method both with phantom and real datasets and we show that it is more efficient than previous methods based on the voxel model, and other alternative models.

M3 - Paper

SP - 18

EP - 26

ER -