A 3D cortical surface mesh exists simply as a collection of connected polygons. The purpose of segmenting such a mesh is to impose a higher level structure which represents something about the underlying structure of the mesh itself. This segmentation should reduce the mesh into "meaningful," connected pieces. In this paper, segmentation using the watershed algorithm is implemented on brain cortical surface meshes. The height function used is a curvature measure inherent in the geometry of the mesh. Four different curvature measures are compared: mean, gaussian, absolute, and root mean square.
pdf
Images used in paper:
| Histograms: |
Mean |
Gaussian |
Absolute |
RMS |
| Segmented Top Views: | Mean | Gaussian | Absolute | RMS |
| Segmented Lateral Views: | Mean | Gaussian | Absolute | RMS |
| Segmented Top Views (sphere): | Mean | Gaussian | Absolute | RMS |
Page last modified on: Saturday, 25-Oct-2003 20:23:49 CDT
