Abstract
Segmentation and representation of the human cerebral cortex from magnetic resonance (MR) images play an important role in neuroscience and medicine. A successful segmentation method must be robust to various imaging artifacts and produce anatomically meaningful and consistent cortical representations. A method for the automatic reconstruction of the inner, central, and outer surfaces of the cerebral cortex from T1-weighted MR brain images is presented. The method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). The algorithm is fast and numerically stable, and yields accurate brain surface reconstructions that are guaranteed to be topologically correct and free from self-intersections. Validation results on real MR data are presented to demonstrate the performance of the method.
Original language | English (US) |
---|---|
Pages (from-to) | 997-1012 |
Number of pages | 16 |
Journal | NeuroImage |
Volume | 23 |
Issue number | 3 |
DOIs | |
State | Published - Nov 2004 |
Keywords
- Cerebral cortex
- Cortical reconstruction
- Human brain mapping
- Magnetic resonance
- T1-weighted MR brain images
ASJC Scopus subject areas
- Neurology
- Cognitive Neuroscience