ABSTRACT
Tensor datasets are common in different application domains. Properties of 3D surfaces, such as curvature, can be described by curvature tensors. The overall process that constructs visualizations of the anatomical structures of interest starting from the measured diffusion data is known as diffusion tensor imaging and is an active area of research in scientific visualization and medical imaging. A better alternative to visualizing the tensor matrix components is to focus on data derived from these components that has a more intuitive physical significance. The method for visualizing tensor data presented next is a generalization of the glyph concept used for visualizing vectors. Tensor glyphs are a probably one of the simplest ways to visualize tensor datasets. However, since they produce a sampled, discontinuous image, tensor glyph visualizations suffer from the same problems as vector glyphs. Tensor glyphs did visualize the information, but lacked, just as their vector counterparts, the spatial continuity of streamlines.
