Cortical surface reconstruction using a topology preserving geometric deformable model

Xiao Han, Chenyang Xu, Duygu Tosun, Jerry Ladd Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis
EditorsL. Staib
Pages213-220
Number of pages8
StatePublished - 2001
EventWorkshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001 - Kauai, HI, United States
Duration: Dec 9 2001Dec 10 2001

Other

OtherWorkshop on Mathematical Methods in Biomedical Image Analysis MMBIA 2001
CountryUnited States
CityKauai, HI
Period12/9/0112/10/01

Fingerprint

Deformable Models
Surface Reconstruction
Geometric Model
Self-intersection
Topology
Magnetic Resonance Image
Level Set Method
Prior Information
Cortex
Landmarks
Image Analysis
Model
Brain

ASJC Scopus subject areas

  • Analysis

Cite this

Han, X., Xu, C., Tosun, D., & Prince, J. L. (2001). Cortical surface reconstruction using a topology preserving geometric deformable model. In L. Staib (Ed.), 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.

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis. ed. / L. Staib. 2001. p. 213-220.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Han, X, Xu, C, Tosun, D & Prince, JL 2001, Cortical surface reconstruction using a topology preserving geometric deformable model. in L Staib (ed.), 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.
Han X, Xu C, Tosun D, Prince JL. Cortical surface reconstruction using a topology preserving geometric deformable model. In Staib L, editor, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis. 2001. p. 213-220
Han, Xiao ; Xu, Chenyang ; Tosun, Duygu ; Prince, Jerry Ladd. / Cortical surface reconstruction using a topology preserving geometric deformable model. Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis. editor / L. Staib. 2001. pp. 213-220
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