Figure 1: Volume renderings of a 64 3 synthetic volume with four different curvature measures. Left to right: first principal curvature κ

Illustrations play a major role in the education process. Whether used to teach a surgical or radiologic procedure, to illustrate normal or aberrant anatomy, or to explain the functioning of a technical device, illustration significantly impacts learning. Although many specimens are readily available as volumetric data sets, particularly in medicine, illustrations are commonly produced manually as static images in a time-consuming process. Our goal is to create a fully dynamic three-dimensional illustration environment which directly operates on volume data. Single images have the aesthetic appeal of traditional illustrations, but can be interactively altered and explored. In this paper we present methods to realize such a system which combines artistic visual styles and expressive visualization techniques. We introduce a novel concept for direct multi-object volume visualization which allows control of the appearance of inter-penetrating objects via two-dimensional transfer functions. Furthermore, a unifying approach to efficiently integrate many non-photorealistic rendering models is presented. We discuss several illustrative concepts which can be realized by combining cutaways, ghosting, and selective deformation. Finally, we also propose a simple interface to specify objects of interest through three-dimensional volumetric painting. All presented methods are integrated into VolumeShop, an interactive hardware-accelerated application for direct volume illustration.

One of the most important goals in volume rendering is to be able to visually separate and selectively enable specific objects of interest contained in a single volumetric data set. Using explicit segmentation information is a very powerful way to approach this problem. We show how segmented volume data sets can be rendered interactively on current consumer graphics hardware with high image quality. Pixel-resolution filtering of object boundaries is supported although only a single segmentation volume for all objects is required. In order to enhance object perception, we employ different levels of object distinction. First, each object can be assigned an individual transfer function, multiple of which can be applied in a single rendering pass. Second, different rendering modes such as direct volume rendering, iso-surfacing, and non-photorealistic techniques can be selected for each object. A minimal number of rendering passes is achieved by processing sets of objects that share the same rendering mode in a single pass. Third, local compositing modes such as alpha blending and MIP can be selected for each object in addition to a single global mode, thus yielding a high-quality hardware implementation of two-level volume rendering.

This paper introduces importance-driven volume rendering as a novel technique for automatic focus and context display of volumetric data. It is a generalization of cut-away views, which - depending on the viewpoint - remove or suppress less important parts of a scene to reveal more important underlying information. We automatize and apply this idea to volumetric data. Each part of the volumetric data is assigned an object importance which encodes visibility priority. It determines which structures should be readily discernable and which structures are less important. In those image regions, where an object occludes more important structures it is displayed more sparsely than in those areas where no occlusion occurs. Thus the objects of interest are clearly visible. For each object several representations, i.e., levels of sparseness, are specified. The display of an individual object may incorporate different levels of sparseness. The goal is to emphasize important structures and to maximize the information content in the final image. This paper also discusses several possible schemes for level of sparseness specification and different ways how object importance can be composited to determine the final appearance of a particular object.

Simulating hand-drawn illustration techniques can succinctly express information in a manner that is communicative and informative. We present a framework for an interactive direct volume illustration system that simulates traditional stipple drawing. By combining the principles of artistic and scientific illustration, we explore several feature enhancement techniques to create effective, interactive visualizations of scientific and medical datasets. We also introduce a rendering mechanism that generates appropriate point lists at all resolutions during an automatic preprocess, and modifies rendering styles through different combinations of these feature enhancements. The new system is an effective way to interactively preview large, complex volume datasets in a concise, meaningful, and illustrative manner. Volume stippling is effective for many applications and provides a quick and efficient method to investigate volume models.

In this paper we present a fast visualization technique for volumetric data, which is based on a recent nonphotorealistic rendering technique. Our new approach enables alternative insights into 3D data sets (compared to traditional approaches such as direct volume rendering or iso-surface rendering). Object contours, which usually are characterized by locally high gradient values, are visualized regardless of their density values. Cumbersome tuning of transfer functions, as usually needed for setting up DVR views is avoided. Instead, a small number of parameters is available to adjust the non-photorealistic display. Based on the magnitude of local gradient information as well as on the angle between viewing direction and gradient vector, data values are mapped to visual properties (color, opacity), which then are combined to form the rendered image (MIP is proposed as the default compositing stragtegy here). Due to the fast implementation of this alternative rendering approach, it is possible to interactively investigate the 3D data, and quickly learn about internal structures. Several further extensions of our new approach, such as level lines are also presented in this paper. Key words: interactive volume rendering, non-photorealistic rendering, shear-warp projection. 1.

This paper introduces importance-driven volume rendering as a novel technique for automatic focus and context display of volumetric data. Our technique is a generalization of cut-away views, which – depending on the viewpoint or feature – remove or suppress less important parts of a scene to reveal more important underlying information. We automatize and apply this idea to volumetric data. Each part of the volumetric data is assigned an object importance which encodes visibility priority. This property determines which structures should be readily discernible and which structures are less important. In those image regions, where an object occludes more important structures it is displayed more sparsely than in those areas where no occlusion occurs. Thus the objects of interest are clearly visible. For each object several representations, i.e., levels of sparseness, are specified. The display of an individual object may incorporate different levels of sparseness. The goal is to emphasize important structures and to maximize the information content in the final image. This paper also discusses several possible schemes for level of sparseness specification and different ways how object importance can be composited to determine the final appearance of a particular object.

Non-photorealistic rendering can be used to illustrate subtle spatial relationships that might not be visible with more realistic rendering techniques. We present a parallel hardware-accelerated rendering technique, making extensive use of multi-texturing and paletted textures, for the interactive non-photorealistic visualization of scalar volume data. With this technique, we can render a 512 512 512 volume using non-photorealistic techniques that include tone-shading, silhouettes, gradient-based enhancement, and color depth cueing, as shown in the images on the color plate, at multiple frames second. The interactivity we achieve with our method allows for the exploration of a large visualization parameter space for the creation of effective illustrations.

Abstract—Visualization can provide valuable assistance for data analysis and decision making tasks. However, how people perceive and interact with a visualization tool can strongly influence their understanding of the data as well as the system’s usefulness. Human factors therefore contribute significantly to the visualization process and should play an important role in the design and evaluation of visualization tools. Several research initiatives have begun to explore human factors in visualization, particularly in perception-based design. Nonetheless, visualization work involving human factors is in its infancy, and many potentially promising areas have yet to be explored. Therefore, this paper aims to 1) review known methodology for doing human factors research, with specific emphasis on visualization, 2) review current human factors research in visualization to provide a basis for future investigation, and 3) identify promising areas for future research. Index Terms—Human factors, visualization, perception, cognitive support, methodology. 1

In volume rendering it is very difficult to simultaneously visualize interior and exterior structures while preserving clear shape cues. Very transparent transfer functions produce cluttered images with many overlapping structures, while clipping techniques completely remove possibly important context information. In this paper we present a new model for volume rendering, inspired by techniques from illustration that provides a means of interactively inspecting the interior of a volumetric data set in a feature-driven way which retains context information. The context-preserving volume rendering model uses a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. This new method represents an alternative to conventional clipping techniques, shares their easy and intuitive user control, but does not suffer from the drawback of missing context information.