Geodesic Distance-weighted Shape Vector Image Diffusion
IEEE Vis 2008
Jing Hua, Zhaoqiang Lai, Ming Dong, Xianfeng Gu and Hong Qin
This paper presents a novel and efficient surface matching and visualization framework through the geodesic distanceweighted
shape vector image diffusion. Based on conformal geometry, our approach can uniquely map a 3D surface to a canonical
rectangular domain and encode the shape characteristics (e.g., mean curvatures and conformal factors) of the surface in the 2D
domain to construct a geodesic distance-weighted shape vector image, where the distances between sampling pixels are not uniform
but the actual geodesic distances on the manifold. Through the novel geodesic distance-weighted shape vector image diffusion
presented in this paper, we can create a multiscale diffusion space, in which the cross-scale extrema can be detected as the robust
geometric features for the matching and registration of surfaces. Therefore, statistical analysis and visualization of surface properties
across subjects become readily available. The experiments on scanned surface models show that our method is very robust for
feature extraction and surface matching even under noise and resolution change. We have also applied the framework on the real
3D human neocortical surfaces, and demonstrated the excellent performance of our approach in statistical analysis and integrated
visualization of the multimodality volumetric data over the shape vector image.