A fast mesh deformation method for neuroanatomical surface inflated representations

Authors:
Andrea Rueda;Álvaro Perea;Daniel Rodríguez-Pérez;Eduardo Romero
Affiliations:
Universidad Nacional de Colombia, Bogotá, Colombia;Department of Mathematical Physics and Fluids, Universidad Nacional de Educación a Distancia, Madrid, Spain;Department of Mathematical Physics and Fluids, Universidad Nacional de Educación a Distancia, Madrid, Spain;Universidad Nacional de Colombia, Bogotá, Colombia
Venue:
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Year:
2007

Citing 5
Cited 1

Constraints on deformable models: recovering 3D shape and nongrid motion

Artificial Intelligence
Numerical recipes in C (2nd ed.): the art of scientific computing

Numerical recipes in C (2nd ed.): the art of scientific computing
Geometric surface smoothing via anisotropic diffusion of normals

Proceedings of the conference on Visualization '02
Conformal Surface Parameterization for Texture Mapping

IEEE Transactions on Visualization and Computer Graphics
Computerized mappings of the cerebral cortex: A multiresolution flattening method and a surface-based coordinate system

Journal of Cognitive Neuroscience

A quantitative comparison of three methods for inflating cortical meshes

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new metric preserving deformation method which permits to generate smoothed representations of neuroanatomical structures. These surfaces are approximated by triangulated meshes which are evolved using an external velocity field, modified by a local curvature dependent contribution. This motion conserves local metric properties since the external force is modified by explicitely including an area preserving term into the motion equation. We show its applicability by computing inflated representations from real neuroanatomical data and obtaining smoothed surfaces whose local area distortion is less than a 5%, when comparing with the original ones.