Author(s)

Kamel Aloui, Amine Naït-Ali, Mohamed Saber Naceur

LiSSi EA, Laboratory, Université Paris-Est Créteil (UPEC), Créteil, France.
LTSIRS Laboratory, National Engineering School, Tunis, Tunisia.

In this paper we present a novel approach for brain surfacec characterization based on convexity and concavity analysis of cortical surface mesh. Initially, volumetric Magnetic Resonance Images (MRI) data is processed to generate a discrete representation of cortical surface using low-level segmentation tools and Level-Sets method. Afterward, pipeline procedure for brain characterization/labeling is developed. The first characterization method is based on discrete curvature classification. This is consists on estimating curvature information at each vertex in the cortical surface mesh. The second method is based on transforming the brain surface mesh into Digital Elevation Model (DEM), where each vertex is designed by its space coordinates and geometric measures related to a reference surface. In other word, it consists on analyzing the cortical surface as a topological map or an elevation map where the ridge or crest lines represent cortical gyri and valley lines represents sulci. The experimental results have shown the importance of these characterization methods for the detection of significant details related to the cortical surface.

Brain; Curvature; DEN; Level-Sets; MRI; Mapping; Surface Mesh

Aloui, K. , Naït-Ali, A. and Naceur, M. (2012) Characterization of a human brain cortical surface mesh using discrete curvature classification and digital elevation model. Journal of Biomedical Science and Engineering, 5, 133-140. doi: 10.4236/jbise.2012.53017.