Abstract
Segmentation and representation of the human cerebral cortex from magnetic resonance images is an important goal in neuroscience and medicine. Accurate cortical segmentation requires preprocessing of the image data to separate certain subcortical structures from the cortex in order to generate a good initial white-matter/gray-matter interface. This step is typically manual or semi-automatic. In this paper, we propose an automatic procedure that is based on a careful analysis of the brain anatomy. Following a fuzzy segmentation of the brain image, the method first extracts the ventricles using a geometric deformable surface model. A region force, derived from the cerebrospinal membership function, is used to deform the surface towards the boundary of the ventricles, while a curvature force controls the smoothness of the surface and prevents it from growing into the outer pial surface. Next, region-growing identifies and fills the subcortical regions in each cortical slice using the detected ventricles as seeds and the white matter and several automatically determined sealing lines as boundaries. To make the method robust to segmentation artifacts, a putamen mask drawn in the Talairach coordinate system is also used to help the region growing process. Visual inspection and initial results on 15 subjects show the success of the proposed method.
Original language | English (US) |
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Pages (from-to) | 194-203 |
Number of pages | 10 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 4322 |
Issue number | 1 |
DOIs | |
State | Published - 2001 |
Event | Medical Imaging 2001 Image Processing - San Diego, CA, United States Duration: Feb 19 2001 → Feb 22 2001 |
Keywords
- Automated image segmentation
- Brain atlas
- Cortical surface reconstruction
- Geometric deformable surface model
- MRI brain images
- Subcortical structures
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering