Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Computational harmonic analysis for tensor fields on the two-sphere
Journal of Computational Physics
FFTs for the 2-Sphere-Improvements and Variations
FFTs for the 2-Sphere-Improvements and Variations
Shape Analysis of Brain Ventricles Using SPHARM
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Removing excess topology from isosurfaces
ACM Transactions on Graphics (TOG)
Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Journal of Cognitive Neuroscience
Multi-scale voxel-based morphometry via weighted spherical harmonic representation
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Topology noise removal for curve and surface evolution
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Hi-index | 0.00 |
A brain surface reconstruction allows advanced analysis of structural and functional brain data that is not possible using volumetric data alone. However, the generation of a brain surface mesh from MRI data often introduces topological defects and artifacts that must be corrected. We show that it is possible to accurately correct these errors using spherical harmonics. Our results clearly demonstrate that brain surface meshes reconstructed using spherical harmonics are free from topological defects and large artifacts that were present in the uncorrected brain surface. Visual inspection reveals that the corrected surfaces are of very high quality. The spherical harmonic surfaces are also quantitatively validated by comparing the surfaces to an "ideal" brain based on a manually corrected average of twelve scans of the same subject. In conclusion, the spherical harmonics approach is a direct, computationally fast method to correct topological errors.