The complexity of todays visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for insta

Abstract Direct Volume Rendering (DVR) provides the possibility to visualize volumetric data sets as they occur in many scientific disciplines. With DVR semi-transparency is facilitated to convey the complexity of the data. Unfortunately, semi-transparency introduces challenges in spatial comprehension of the data, as the ambiguities inherent to semi-transparent representations affect spatial comprehension. Accordingly, many techniques have been introduced to enhance the spatial comprehension of DVR images. In this paper, we present our findings obtained from two evaluations investigating the perception of semi-transparent structures from volume rendered images. We have conducted a user evaluation in which we have compared standard DVR with five techniques previously proposed to enhance the spatial comprehension of DVR images. In this study, we investigated the perceptual performance of these techniques and have compared them against each other in a large-scale quantitative user study with 300 participants. Each participant completed micro-tasks designed such that the aggregated feedback gives insight on how well these techniques aid the user to perceive depth and shape of objects. To further clarify the findings, we conducted a qualitative evaluation in which we interviewed three experienced visualization researchers, in order to find out if we can identify the benefits and shortcomings of the individual techniques.

We present a concept for interactive rendering of multiple data sets of varying type, including geometry and volumetric data, in one scene with correct transparency. Typical visualization applications involve multiple data fields from various sources. A thorough understanding of such data often requires combined rendering of theses fields. The choice of the visualization concepts, and thus the rendering techniques, depends on the context and type of the individual fields. Efficiently combining different techniques in one scene, however, is not always a straightforward task. We tackle this problem by using an A-buffer based approach to gather color and transparency information from different sources, combine them and generate the final output image. Thereby we put special emphasis on efficiency and low memory consumption to allow a smooth exploration of the data. Therefore, we compare different A-buffer implementations with respect to memory consumption and memory access pattern. Additionally we introduce an early-fragment-discarding heuristic using inter-frame information to speed up the rendering..

User evaluations have gained increasing importance in visualization research over the past years, as in many cases these evaluations are the only way to support the claims made by visualization researchers. Unfortunately, recent literature reviews show that in comparison to algorithmic performance evaluations, the number of user evaluations is still very low. Reasons for this are the required amount of time to conduct such studies together with the difficulties involved in participant recruitment and result reporting. While it could be shown that the quality of evaluation results and the simplified participant recruitment of crowdsourcing platforms makes this technology a viable alternative to lab experiments when evaluating visualizations, the time for conducting and reporting such evaluations is still very high. In this paper, we propose a software system, which integrates the conduction, the analysis and the reporting of crowdsourced user evaluations directly into the scientific visualization development process. With the proposed system, researchers can conduct and analyze quantitative evaluations on a large scale through an evaluation-centric user interface with only a few mouse clicks. Thus, it becomes possible to perform iterative evaluations during algorithm design, which potentially leads to better results, as compared to the time consuming user evaluations traditionally conducted at the end of the design process. Furthermore, the system is built around a centralized database, which supports an easy reuse of old evaluation designs and the reproduction of old evaluations with new or additional stimuli, which are both driving challenges in scientific visualization research. We will describe the system's design and the considerations made during the design process, and demonstrate the system by conducting three user evaluations, all of which have been published before in the visualization literature.

Blood flow data from direct measurements (4D flow MRI) or numerical simulations opens new possibilities for the understanding of the development of cardiac diseases. However, before this new data can be used in clinical studies or for diagnosis, it is important to develop a notion of the characteristics of typical flow structures. To support this process we developed a novel blood flow clustering and exploration method. The method builds on the concept of coherent flow structures. Coherence maps for cross-sectional slices are defined to show the overall degree of coherence of the flow. In coherent regions the method summarizes the dominant blood flow using a small number of pathline representatives. In contrast to other clustering approaches the clustering is restricted to coherent regions and pathlines with low coherence values, which are not suitable for clustering and thus are not forced into clusters. The coherence map is based on the Finite-time Lyapunov Exponent (FTLE). It is created on selected planes in the inflow respective outflow area of a region of interest. The FTLE value measures the rate of separation of pathlines originating from this plane. Different to previous work using FTLE we do not focus on separating extremal lines but on local minima and regions of low FTLE intensities to extract coherent flow. The coherence map and the extracted clusters serve as basis for the flow exploration. The extracted clusters can be selected and inspected individually. Their flow rate and coherence provide a measure for their significance. Switching off clusters reduces the amount of occlusion and reveals the remaining part of the flow. The non-coherent regions can also be explored by interactive manual pathline seeding in the coherence map.

Data acquired from ultrasound examinations is of interest not only for the physician, but also for the patient. While the physician uses the ultrasound data for diagnostic purposes the patient might be more interested in beautiful images in the case of prenatal imaging. Ultrasound data is noisy by nature and visually compelling 3D renderings are not always trivial to produce. This paper presents a technique which enables extraction of a smooth surface mesh from the ultrasound data by combining previous research in ultrasound processing with research in point cloud surface reconstruction. After filtering the ultrasound data using Variational Classification we extract a set of surface points. This set of points is then used to train an Adaptive Compactly Supported Radial Basis Functions system, a technique for surface reconstruction of noisy laser scan data. The resulting technique can be used to extract surfaces with adjustable smoothness and resolution and has been tested on various ultrasound datasets.