We present a parallel visualization algorithm for the illustrative rendering of depth-dependent stylized dense tube data at interactive frame rates. While this computation could be efficiently performed on a GPU device, we target a parallel framework to enable it to be efficiently running on an ordinary multi-core CPU platform which is much more available than GPUs for common users. Our approach is to map the depth information in each tube onto each of the visual dimensions of shape, color, texture, value, and size on the basis of Bertin's semiology theory. The purpose is to enable more legible displays in the dense tube environments. A major contribution of our work is an efficient and effective parallel depthordering algorithm that makes use of the message passing interface (MPI) with VTK. We evaluated our framework with visualizations of depth-stylized tubes derived from 3D diffusion tensor MRI data by comparing its efficiency with several other alternative parallelization platforms running the same computations. As our results show, the parallelization framework we proposed can efficiently render highly dense 3D data sets like the tube data and thus is useful as a complement to parallel visualization environments that rely on GPUs.
We present real-time vascular visualization methods, which extend on illustrative rendering techniques to particularly accentuate spatial depth and to improve the perceptive separation of important vascular properties such as branching level and supply area. The resulting visualization can and has already been used for direct projection on a patient's organ in the operation theater where the varying absorption and reflection characteristics of the surface limit the use of color. The important contributions of our work are a GPU-based hatching algorithm for complex tubular structures that emphasizes shape and depth as well as GPU-accelerated shadow-like depth indicators, which enable reliable comparisons of depth distances in a static monoscopic 3D visualization. In addition, we verify the expressiveness of our illustration methods in a large, quantitative study with 160 subjects