### Abstract

Accurate reconstruction of the cortical surface of the brain from magnetic resonance images is an important objective in biomedical image analysis. Parametric deformable surface models are usually used because they incorporate prior information, yield subvoxel accuracy, and automatically preserve topology. These algorithms are very computationally costly, however, particularly if self-intersection prevention is imposed. Geometric deformable surface models, implemented using level set methods, are computationally fast and are automatically free front self-intersections, but are unable to guarantee the correct topology. This paper describes both a new geometric deformable surface model which preserves topology and an overall strategy for reconstructing the inner, central, and outer surfaces of the brain cortex. The resulting algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self intersections. We ran the algorithm on 21 data sets and show detailed results for a typical data set. We also show a preliminary validation using landmarks manually placed as a truth model on six of the data sets.

Original language | English (US) |
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Title of host publication | Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis |

Editors | L. Staib |

Pages | 213-220 |

Number of pages | 8 |

State | Published - 2001 |

Event | Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001 - Kauai, HI, United States Duration: Dec 9 2001 → Dec 10 2001 |

### Other

Other | Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001 |
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Country | United States |

City | Kauai, HI |

Period | 12/9/01 → 12/10/01 |

### Fingerprint

### ASJC Scopus subject areas

- Analysis

### Cite this

*Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis*(pp. 213-220)

**Cortical surface reconstruction using a topology preserving geometric deformable model.** / Han, Xiao; Xu, Chenyang; Tosun, Duygu; Prince, Jerry Ladd.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.*pp. 213-220, Workshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001, Kauai, HI, United States, 12/9/01.

}

TY - GEN

T1 - Cortical surface reconstruction using a topology preserving geometric deformable model

AU - Han, Xiao

AU - Xu, Chenyang

AU - Tosun, Duygu

AU - Prince, Jerry Ladd

PY - 2001

Y1 - 2001

N2 - Accurate reconstruction of the cortical surface of the brain from magnetic resonance images is an important objective in biomedical image analysis. Parametric deformable surface models are usually used because they incorporate prior information, yield subvoxel accuracy, and automatically preserve topology. These algorithms are very computationally costly, however, particularly if self-intersection prevention is imposed. Geometric deformable surface models, implemented using level set methods, are computationally fast and are automatically free front self-intersections, but are unable to guarantee the correct topology. This paper describes both a new geometric deformable surface model which preserves topology and an overall strategy for reconstructing the inner, central, and outer surfaces of the brain cortex. The resulting algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self intersections. We ran the algorithm on 21 data sets and show detailed results for a typical data set. We also show a preliminary validation using landmarks manually placed as a truth model on six of the data sets.

AB - Accurate reconstruction of the cortical surface of the brain from magnetic resonance images is an important objective in biomedical image analysis. Parametric deformable surface models are usually used because they incorporate prior information, yield subvoxel accuracy, and automatically preserve topology. These algorithms are very computationally costly, however, particularly if self-intersection prevention is imposed. Geometric deformable surface models, implemented using level set methods, are computationally fast and are automatically free front self-intersections, but are unable to guarantee the correct topology. This paper describes both a new geometric deformable surface model which preserves topology and an overall strategy for reconstructing the inner, central, and outer surfaces of the brain cortex. The resulting algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self intersections. We ran the algorithm on 21 data sets and show detailed results for a typical data set. We also show a preliminary validation using landmarks manually placed as a truth model on six of the data sets.

UR - http://www.scopus.com/inward/record.url?scp=0035700464&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035700464&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0035700464

SP - 213

EP - 220

BT - Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

A2 - Staib, L.

ER -