Approximation of optimal surface parameterizations and the application in cerebral cortex mapping.
Optimal parameterizations of surface meshes are useful in the mapping and visualization of the cerebral cortex, the outer layer of the human brain. We propose two new methods to compute approximations of the optimal parameterizations, and apply these methods to human cortical surface meshes extracted from magnetic resonance images. Our methods approximate the parameterizations in a low-dimensional subspace spanned by the coordinate vectors of an initial parameterization and the low-frequency eigenvectors of a mesh Laplacian. This low-dimensional approximation reduces the computational complexity while minimizing the error.
Vadakkumpadan
Fijoy
F
Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA. fijoy@jhu.edu
Spellucci
Peter
P
Sun
Yinlong
Y
D000465:Algorithms; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008959:Models, Neurological; D010363:Pattern Recognition, Automated; D013382:Subtraction Technique