Datasets with large heterogeneity in data density are prone to result in visualizations with unutilized whitespace and overplotting. Sequential data, such as in genomics or electronic health records, often has such density distributions, containing long empty stretches between data-rich regions. For these data types, the exact distance is not necessarily the central feature, and plotting along a linear axis causes occlusion of the actual datapoints. In this work, we evaluate content-driven axis compression for minimizing whitespace and reducing occlusion while maintaining distance information. The axis is compressed based on the density of the data, and it requires minimal a priori knowledge of the data distribution. We present a framework for reasoning about the benefits and drawbacks of using axis compression. The framework includes reasoning about the separability of items and clusters, suggests careful consideration of how the position visual channel is utilized, and highlights the influence of the usage context for which the visualization is designed. We conducted a study to evaluate the strengths and weaknesses of axis compression and found that it leads to improved item visibility while maintaining high accuracy, also for tasks targeted at understanding the data distribution.

We present VOICE, a novel approach to science communication that connects large language models' conversational capabilities with interactive exploratory visualization. VOICE introduces several innovative technical contributions that drive our conversational visualization framework. Based on the collected design requirements, we introduce a two-layer agent architecture that can perform task assignment, instruction extraction, and coherent content generation. We employ fine-tuning and prompt engineering techniques to tailor agents' performance to their specific roles and accurately respond to user queries. Our interactive text-to-visualization method generates a flythrough sequence matching the content explanation. In addition, natural language interaction provides capabilities to navigate and manipulate 3D models in real-time. The VOICE framework can receive arbitrary voice commands from the user and respond verbally, tightly coupled with a corresponding visual representation, with low latency and high accuracy. We demonstrate the effectiveness of our approach by implementing a proof-of-concept prototype and applying it to the molecular visualization domain: analyzing three 3D molecular models with multiscale and multi-instance attributes. Finally, we conduct a comprehensive evaluation of the system, including quantitative and qualitative analyses on our collected dataset, along with a detailed public user study and expert interviews. The results confirm that our framework and prototype effectively meet the design requirements and cater to the needs of diverse target users.

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow with qualitative feedback from experts.

Genomics is at the core of precision medicine, and there are high expectations on genomics-enabled improvement of patient outcomes in the years to come. Around the world, initiatives to increase the use of DNA sequencing in clinical routine are being deployed, such as the use of broad panels in the standard care for oncology patients. Such a development comes at the cost of increased demands on throughput in genomic data analysis. In this paper, we use the task of copy number variant (CNV) analysis as a context for exploring visualization concepts for clinical genomics. CNV calls are generated algorithmically, but time-consuming manual intervention is needed to separate relevant findings from irrelevant ones in the resulting large call candidate lists. We present a visualization environment, named Copycat, to support this review task in a clinical scenario. Key components are a scatter-glyph plot replacing the traditional list visualization, and a glyph representation designed for at-a-glance relevance assessments. Moreover, we present results from a formative evaluation of the prototype by domain specialists, from which we elicit insights to guide both prototype improvements and visualization for clinical genomics in general.

An essential task of an air traffic controller is to manage the traffic flow by predicting future trajectories. Complex traffic patterns are difficult to predict and manage and impose cognitive load on the air traffic controllers. In this work we present an interactive visual analytics interface which facilitates detection and resolution of complex traffic patterns for air traffic controllers. The interface supports air traffic controllers in detecting complex clusters of aircraft and further enables them to visualize and simultaneously compare how different re-routing strategies for each individual aircraft yield reduction of complexity in the entire sector for the next hour. The development of the concepts was supported by the domain-specific feedback we received from six fully licensed and operational air traffic controllers in an iterative design process over a period of 14 months.

Abstract Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.

Abstract One of the commonly used visualization techniques for multivariate data is the parallel coordinates plot. It provides users with a visual overview of multivariate data and the possibility to interactively explore it. While pattern recognition is a strength of the human visual system, it is also a strength of the auditory system. Inspired by the integration of the visual and auditory perception in everyday life, we introduce an audio-visual analytics design named Parallel Chords combining both visual and auditory displays. Parallel Chords lets users explore multivariate data using both visualization and sonification through the interaction with the axes of a parallel coordinates plot. To illustrate the potential of the design, we present (1) prototypical data patterns where the sonification helps with the identification of correlations, clusters, and outliers, (2) a usage scenario showing the sonification of data from non-adjacent axes, and (3) a controlled experiment on the sensitivity thresholds of participants when distinguishing the strength of correlations. During this controlled experiment, 35 participants used three different display types, the visualization, the sonification, and the combination of these, to identify the strongest out of three correlations. The results show that all three display types enabled the participants to identify the strongest correlation — with visualization resulting in the best sensitivity. The sonification resulted in sensitivities that were independent from the type of displayed correlation, and the combination resulted in increased enjoyability during usage.

Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are distorted or masked by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate a set of optimized data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, named neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM’s effectiveness through experiments and case studies using synthetic and real-world datasets.

We present Moliverse, an integration of the molecular visualization framework VIAMD into the astronomical visualization software OpenSpace, allowing us to bridge the two extreme ends of the scale spectrum to show, for example, the gas composition in a planet’s atmosphere or molecular structures in comet trails and can empower the creation of educational exhibitions. For that purpose we do not use a linear scale traversal but break the scale continuity and show molecular simulations as focus in the context of celestial bodies. We demonstrate the application of our concept in two storytelling scenarios and envision the application both for science presentations to lay audiences and for dedicated exploration, potentially also in a molecule-only environment.

Abstract Operators in air traffic control facing time‐ and safety‐critical situations call for efficient, reliable and robust real‐time processing and interpretation of complex data. Automation support tools aid controllers in these processes to prevent separation losses between aircraft. Issues of current support tools include limited ‘what‐if’ and ‘what‐else’ probe functionalities in relation to vertical solutions. This work presents the design and evaluation of two visual analytics interfaces that promote contextual awareness and support ‘what‐if’ and ‘what‐else’ probes in the spatio‐temporal domain aiming to improve information integration and support controllers in prioritising conflict resolution. Both interfaces visualize vertical solution spaces against a time‐altitude graph. The main contributions of this paper are: (a) the presentation of two interfaces for supporting conflict solving; (b) the novel representation of how vertical information and aircraft rate of climb and descent affect conflicts and (c) an evaluation and comparison of the interfaces with a traditional air traffic control support system. The evaluation study was performed with domain experts to compare the effects of visualization concepts on operator engagement in processing solutions suggested by the tools. Results show that the visualizations support operators' ability to understand and resolve conflicts. Based on the results, general design guidelines for time‐critical domains are proposed.

This February 2022 issue of theIEEE Transactions on Visualization and Computer Graphics (TVCG)contains the proceedings of IEEE VIS 2021, held online on October 24-29, 2021, with General Chairs from Tulane University and Universidade de Sao Paulo. With IEEE VIS 2021, the conference series is in its 32nd year.

In molecular analysis, spatial distribution functions (SDF) are fundamental instruments in answering questions related to spatial occurrences and relations of atomic structures over time. Given a molecular trajectory, SDFs can, for example, reveal the occurrence of water in relation to particular structures and hence provide clues of hydrophobic and hydrophilic regions. For the computation of meaningful distribution functions, the definition of molecular reference structures is essential. Therefore we introduce the concept of an internal frame of reference (IFR) for labeled point sets that represent selected molecular structures, and we propose an algorithm for tracking the IFR over time and space using a variant of Kabsch's algorithm. This approach lets us generate a consistent space for the aggregation of the SDF for molecular trajectories and molecular ensembles. We demonstrate the usefulness of the technique by applying it to temporal molecular trajectories as well as ensemble datasets. The examples include different docking scenarios with DNA, insulin, and aspirin.

Abstract We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.

Abstract We present a state‐of‐the‐art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.

We present an atmospheric model tailored for the interactive visualization of planetary surfaces. As the exploration of the solar system is progressing with increasingly accurate missions and instruments, the faithful visualization of planetary environments is gaining increasing interest in space research, mission planning, and science communication and education. Atmospheric effects are crucial in data analysis and to provide contextual information for planetary data. Our model correctly accounts for the non-linear path of the light inside the atmosphere (in Earth's case), the light absorption effects by molecules and dust particles, such as the ozone layer and the Martian dust, and a wavelength-dependent phase function for Mie scattering. The mode focuses on interactivity, versatility, and customization, and a comprehensive set of interactive controls make it possible to adapt its appearance dynamically. We demonstrate our results using Earth and Mars as examples. However, it can be readily adapted for the exploration of other atmospheres found on, for example, of exoplanets. For Earth's atmosphere, we visually compare our results with pictures taken from the International Space Station and against the CIE clear sky model. The Martian atmosphere is reproduced based on available scientific data, feedback from domain experts, and is compared to images taken by the Curiosity rover. The work presented here has been implemented in the OpenSpace system, which enables interactive parameter setting and real-time feedback visualization targeting presentations in a wide range of environments, from immersive dome theaters to virtual reality headsets.

Common reporting styles for statistical results in scientific articles, such as p-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the p-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments on researchers with expertise in statistical analysis to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in researchers' subjective confidence in the results. We also asked the respondents' opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations - measured as the 'cliff effect': the sudden drop in confidence around p-value 0.05 - compared with classic CI visualization and textual representation of the CI with p-values. All data and analyses are publicly available at https://github.com/helske/statvis.

This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space – the neural weight space. To explore the complex structure of this space, we sample from a diverse selection of training variations (dataset, optimization procedure, architecture, etc.) of neural network classifiers, and train a large number of models to represent the weight space. Then, we use a machine learning approach for analyzing and extracting information from this space. Most centrally, we train a number of novel deep meta-classifiers with the objective of classifying different properties of the training setup by identifying their footprints in the weight space. Thus, the meta-classifiers probe for patterns induced by hyper-parameters, so that we can quantify how much, where, and when these are encoded through the optimization process. This provides a novel and complementary view for explainable AI, and we show how meta-classifiers can reveal a great deal of information about the training setup and optimization, by only considering a small subset of randomly selected consecutive weights. To promote further research on the weight space, we release the neural weight space (NWS) dataset – a collection of 320K weight snapshots from 16K individually trained deep neural networks.

Abstract We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.

Human knowledge about the cosmos is rapidly increasing as instruments and simulations are generating new data supporting the formation of theory and understanding of the vastness and complexity of the universe. OpenSpace is a software system that takes on the mission of providing an integrated view of all these sources of data and supports interactive exploration of the known universe from the millimeter scale showing instruments on spacecrafts to billions of light years when visualizing the early universe. The ambition is to support research in astronomy and space exploration, science communication at museums and in planetariums as well as bringing exploratory astrographics to the class room. There is a multitude of challenges that need to be met in reaching this goal such as the data variety, multiple spatio-temporal scales, collaboration capabilities, etc. Furthermore, the system has to be flexible and modular to enable rapid prototyping and inclusion of new research results or space mission data and thereby shorten the time from discovery to dissemination. To support the different use cases the system has to be hardware agnostic and support a range of platforms and interaction paradigms. In this paper we describe how OpenSpace meets these challenges in an open source effort that is paving the path for the next generation of interactive astrographics.

The complexity of today's 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 instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities.

We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 × 100,000× 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work.

The 2018 Visualization Technical Achievement Award goes to Anders Ynnerman for his contributions to medical visualization resulting in the development of virtual autopsies, which have had extraordinary impact in both in medicine and in communication of science to the public. The IEEE Visualization & Graphics Technical Committee (VGTC) is pleased to award Anders Ynnerman the 2018 Visualization Technical Achievement Award.

Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes, such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters, interactive touch tables, and virtual reality headsets.

In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.

Abstract In this work, we address the challenge of seamlessly visualizing astronomical data exhibiting huge scale differences in distance, size, and resolution. One of the difficulties is accurate, fast, and dynamic positioning and navigation to enable scaling over orders of magnitude, far beyond the precision of floating point arithmetic. To this end we propose a method that utilizes a dynamically assigned frame of reference to provide the highest possible numerical precision for all salient objects in a scene graph. This makes it possible to smoothly navigate and interactively render, for example, surface structures on Mars and the Milky Way simultaneously. Our work is based on an analysis of tracking and quantification of the propagation of precision errors through the computer graphics pipeline using interval arithmetic. Furthermore, we identify sources of precision degradation, leading to incorrect object positions in screen‐space and z‐fighting. Our proposed method operates without near and far planes while maintaining high depth precision through the use of floating point depth buffers. By providing interoperability with order‐independent transparency algorithms, direct volume rendering, and stereoscopy, our approach is well suited for scientific visualization. We provide the mathematical background, a thorough description of the method, and a reference implementation.

We present a method for interactive global illumination of both static and time-varying volumetric data based on reduction of the overhead associated with re-computation of photon maps. Our method uses the identification of photon traces invariant to changes of visual parameters such as the transfer function (TF), or data changes between time-steps in a 4D volume. This lets us operate on a variant subset of the entire photon distribution. The amount of computation required in the two stages of the photon mapping process, namely tracing and gathering, can thus be reduced to the subset that are affected by a data or visual parameter change. We rely on two different types of information from the original data to identify the regions that have changed. A low resolution uniform grid containing the minimum and maximum data values of the original data is derived for each time step. Similarly, for two consecutive time-steps, a low resolution grid containing the difference between the overlapping data is used. We show that this compact metadata can be combined with the transfer function to identify the regions that have changed. Each photon traverses the low-resolution grid to identify if it can be directly transferred to the next photon distribution state or if it needs to be recomputed. An efficient representation of the photon distribution is presented leading to an order of magnitude improved performance of the raycasting step. The utility of the method is demonstrated in several examples that show visual fidelity, as well as performance. The examples show that visual quality can be retained when the fraction of retraced photons is as low as 40%-50%.

Abstract A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state‐of‐the‐art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.

Processing and visualizing large scale volumetric and geometric datasets is mission critical in an increasing number of applications in academic research as well as in commercial enterprise. Often the datasets are, or can be processed to become, sparse. In this paper, we present VoxLink, a novel approach to render sparse volume data in a memory-efficient manner enabling interactive rendering on common, offthe- shelf graphics hardware. Our approach utilizes current GPU architectures for voxelizing, storing, and visualizing such datasets. It is based on the idea of perpixel linked lists (ppLL), an A-buffer implementation for order-independent transparency rendering. The method supports voxelization and rendering of dense semi-transparent geometry, sparse volume data, and implicit surface representations with a unified data structure. The proposed data structure also enables efficient simulation of global lighting effects such as reflection, refraction, and shadow ray evaluation.

In this work we present a volume exploration method designed to be used by novice users and visitors to science centers and museums. The volumetric digitalization of artifacts in museums is of rapidly increasing interest as enhanced user experience through interactive data visualization can be achieved. This is, however, a challenging task since the vast majority of visitors are not familiar with the concepts commonly used in data exploration, such as mapping of visual properties from values in the data domain using transfer functions. Interacting in the data domain is an effective way to filter away undesired information but it is difficult to predict where the values lie in the spatial domain. In this work we make extensive use of dynamic previews instantly generated as the user explores the data domain. The previews allow the user to predict what effect changes in the data domain will have on the rendered image without being aware that visual parameters are set in the data domain. Each preview represents a subrange of the data domain where overview and details are given on demand through zooming and panning. The method has been designed with touch interfaces as the target platform for interaction. We provide a qualitative evaluation performed with visitors to a science center to show the utility of the approach.

Summary form only given. In the last decades imaging modalities have advanced beyond recognition and data of rapidly increasing size and quality can be captured with high speed. This talk will show how data visualization can be used to provide public visitor venues, such as museums, science centers and zoos with unique interactive learning experiences. By combining data visualization techniques with technologies such as interactive multi-touch tables and intuitive user interfaces, visitors can conduct guided browsing of large volumetric image data. The visitors then themselves become the explorers of the normally invisible interior of unique artifacts and subjects. The talk will take its starting point in the current state-of-the-art in CT and MRI scanning technology. It will then discuss the latest high-quality interactive volume rendering and multi-resolution techniques for large scale data and how they are tailored for use in public spaces. Examples will then be shown of how the inside workings of the human body, exotic animals, natural history subjects, such as the martian meteorite, or even mummies can be explored interactively. The recent mummy installation at the British Museum will be shown and discussed from both a curator and visitor perspective and results from a 3 month trial period in the galleries will be presented.

Abstract In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi‐transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A‐buffer, an enhanced framebuffer with additional per‐pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume‐geometry data based on the A‐buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A‐buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather and medical visualization containing both, volumetric data and geometric structures.

Abstract Interactive volume rendering in its standard formulation has become an increasingly important tool in many application domains. In recent years several advanced volumetric illumination techniques to be used in interactive scenarios have been proposed. These techniques claim to have perceptual benefits as well as being capable of producing more realistic volume rendered images. Naturally, they cover a wide spectrum of illumination effects, including varying shading and scattering effects. In this survey, we review and classify the existing techniques for advanced volumetric illumination. The classification will be conducted based on their technical realization, their performance behaviour as well as their perceptual capabilities. Based on the limitations revealed in this review, we will define future challenges in the area of interactive advanced volumetric illumination.

Abstract Molecular visualization is often challenged with rendering of large molecular structures in real time. We introduce a novel approach that enables us to show even large protein complexes. Our method is based on the level‐of‐detail concept, where we exploit three different abstractions combined in one visualization. Firstly, molecular surface abstraction exploits three different surfaces, solvent‐excluded surface (SES), Gaussian kernels and van der Waals spheres, combined as one surface by linear interpolation. Secondly, we introduce three shading abstraction levels and a method for creating seamless transitions between these representations. The SES representation with full shading and added contours stands in focus while on the other side a sphere representation of a cluster of atoms with constant shading and without contours provide the context. Thirdly, we propose a hierarchical abstraction based on a set of clusters formed on molecular atoms. All three abstraction models are driven by one importance function classifying the scene into the near‐, mid‐ and far‐field. Moreover, we introduce a methodology to render the entire molecule directly using the A‐buffer technique, which further improves the performance. The rendering performance is evaluated on series of molecules of varying atom counts.

We propose an approach for verification of volume rendering correctness based on an analysis of the volume rendering integral, the basis of most DVR algorithms. With respect to the most common discretization of this continuous model (Riemann summation), we make assumptions about the impact of parameter changes on the rendered results and derive convergence curves describing the expected behavior. Specifically, we progressively refine the number of samples along the ray, the grid size, and the pixel size, and evaluate how the errors observed during refinement compare against the expected approximation errors. We derive the theoretical foundations of our verification approach, explain how to realize it in practice, and discuss its limitations. We also report the errors identified by our approach when applied to two publicly available volume rendering packages.

In visualization, the combined role of data reconstruction and its classification plays a crucial role. In this paper we propose a novel approach that improves classification of different materials and their boundaries by combining information from the classifiers at the reconstruction stage. Our approach estimates the targeted materials' local support before performing multiple material-specific reconstructions that prevent much of the misclassification traditionally associated with transitional regions and transfer function (TF) design. With respect to previously published methods our approach offers a number of improvements and advantages. For one, it does not rely on TFs acting on derivative expressions, therefore it is less sensitive to noisy data and the classification of a single material does not depend on specialized TF widgets or specifying regions in a multidimensional TF. Additionally, improved classification is attained without increasing TF dimensionality, which promotes scalability to multivariate data. These aspects are also key in maintaining low interaction complexity. The results are simple-to-achieve visualizations that better comply with the user's understanding of discrete features within the studied object.

Illumination is one of the key components in the creation of realistic renderings of scenes containing virtual objects. In this paper, we present a set of novel algorithms and data structures for visualization, processing and rendering with real world lighting conditions captured using High Dynamic Range (HDR) video. The presented algorithms enable rapid construction of general and editable representations of the lighting environment, as well as extraction and fitting of sampled reflectance to parametric BRDF models. For efficient representation and rendering of the sampled lighting environment function, we consider an adaptive (2D/4D) data structure for storage of light field data on proxy geometry describing the scene. To demonstrate the usefulness of the algorithms, they are presented in the context of a fully integrated framework for spatially varying image based lighting. We show reconstructions of example scenes and resulting production quality renderings of virtual furniture with spatially varying real world illumination including occlusions.

This article presents two new parametric models of the Bidirectional Reflectance Distribution Function (BRDF), one inspired by the Rayleigh-Rice theory for light scattering from optically smooth surfaces, and one inspired by micro-facet theory. The models represent scattering from a wide range of glossy surface types with high accuracy. In particular, they enable representation of types of surface scattering which previous parametric models have had trouble modeling accurately. In a study of the scattering behavior of measured reflectance data, we investigate what key properties are needed for a model to accurately represent scattering from glossy surfaces. We investigate different parametrizations and how well they match the behavior of measured BRDFs. We also examine the scattering curves which are represented in parametric models by different distribution functions. Based on the insights gained from the study, the new models are designed to provide accurate fittings to the measured data. Importance sampling schemes are developed for the new models, enabling direct use in existing production pipelines. In the resulting renderings we show that the visual quality achieved by the models matches that of the measured data.

In this paper, we enable interactive volumetric global illumination by extending photon mapping techniques to handle interactive transfer function (TF) and material editing in the context of volume rendering. We propose novel algorithms and data structures for finding and evaluating parts of a scene affected by these parameter changes, and thus support efficient updates of the photon map. In direct volume rendering (DVR) the ability to explore volume data using parameter changes, such as editable TFs, is of key importance. Advanced global illumination techniques are in most cases computationally too expensive, as they prevent the desired interactivity. Our technique decreases the amount of computation caused by parameter changes, by introducing Historygrams which allow us to efficiently reuse previously computed photon media interactions. Along the viewing rays, we utilize properties of the light transport equations to subdivide a view-ray into segments and independently update them when invalid. Unlike segments of a view-ray, photon scattering events within the volumetric medium needs to be sequentially updated. Using our Historygram approach, we can identify the first invalid photon interaction caused by a property change, and thus reuse all valid photon interactions. Combining these two novel concepts, supports interactive editing of parameters when using volumetric photon mapping in the context of DVR. As a consequence, we can handle arbitrarily shaped and positioned light sources, arbitrary phase functions, bidirectional reflectance distribution functions and multiple scattering which has previously not been possible in interactive DVR.

We present an algorithm that enables real-time dynamic shading in direct volume rendering using general lighting, including directional lights, point lights, and environment maps. Real-time performance is achieved by encoding local and global volumetric visibility using spherical harmonic (SH) basis functions stored in an efficient multiresolution grid over the extent of the volume. Our method enables high-frequency shadows in the spatial domain, but is limited to a low-frequency approximation of visibility and illumination in the angular domain. In a first pass, level of detail (LOD) selection in the grid is based on the current transfer function setting. This enables rapid online computation and SH projection of the local spherical distribution of visibility information. Using a piecewise integration of the SH coefficients over the local regions, the global visibility within the volume is then computed. By representing the light sources using their SH projections, the integral over lighting, visibility, and isotropic phase functions can be efficiently computed during rendering. The utility of our method is demonstrated in several examples showing the generality and interactive performance of the approach.

The parameter estimation variance of the Single Point Active Alignment Method (SPAAM) is studied through an experiment where 11 subjects are instructed to create alignments using an Optical See-Through Head Mounted Display (OSTHMD) such that three separate correspondence point distributions are acquired. Modeling the OSTHMD and the subject's dominant eye as a pinhole camera, findings show that a correspondence point distribution well distributed along the user's line of sight yields less variant parameter estimates. The estimated eye point location is studied in particular detail. The findings of the experiment are complemented with simulated data which show that image plane orientation is sensitive to the number of correspondence points. The simulated data also illustrates some interesting properties on the numerical stability of the calibration problem as a function of alignment noise, number of correspondence points, and correspondence point distribution.

Medical imaging plays a central role in a vast range of healthcare practices. The usefulness of 3D visualizations has been demonstrated for many types of treatment planning. Nevertheless, full access to 3D renderings outside of the radiology department is still scarce even for many image-centric specialties. Our work stems from the hypothesis that this under-utilization is partly due to existing visualization systems not taking the prerequisites of this application domain fully into account. We have developed a medical visualization table intended to better fit the clinical reality. The overall design goals were two-fold: similarity to a real physical situation and a very low learning threshold. This paper describes the development of the visualization table with focus on key design decisions. The developed features include two novel interaction components for touch tables. A user study including five orthopedic surgeons demonstrates that the system is appropriate and useful for this application domain.

In recent years, many volumetric illumination models have been proposed, which have the potential to simulate advanced lighting effects and thus support improved image comprehension. Although volume ray-casting is widely accepted as the volume rendering technique which achieves the highest image quality, so far no volumetric illumination algorithm has been designed to be directly incorporated into the ray-casting process. In this paper we propose image plane sweep volume illumination (IPSVI), which allows the integration of advanced illumination effects into a GPU-based volume ray-caster by exploiting the plane sweep paradigm. Thus, we are able to reduce the problem complexity and achieve interactive frame rates, while supporting scattering as well as shadowing. Since all illumination computations are performed directly within a single rendering pass, IPSVI does not require any preprocessing nor does it need to store intermediate results within an illumination volume. It therefore has a significantly lower memory footprint than other techniques. This makes IPSVI directly applicable to large data sets. Furthermore, the integration into a GPU-based ray-caster allows for high image quality as well as improved rendering performance by exploiting early ray termination. This paper discusses the theory behind IPSVI, describes its implementation, demonstrates its visual results and provides performance measurements.

We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). In these implementations the subject for example focuses on a letter while different rows and columns of the virtual keyboard are flashing. The system then tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is not limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary.

A common computer vision task is navigation and mapping. Many indoor navigation tasks require depth knowledge of flat, unstructured surfaces (walls, floor, ceiling). With passive illumination only, this is an ill-posed problem. Inspired by small children using a torchlight, we use a spotlight for active illumination. Using our torchlight approach, depth and orientation estimation of unstructured, flat surfaces boils down to estimation of ellipse parameters. The extraction of ellipses is very robust and requires little computational effort.

Abstract In this paper we study the comprehensive effects on volume rendered images due to numerical errors caused by the use of finite precision for data representation and processing. To estimateactualerror behavior we conduct a thorough study using a volume renderer implemented with arbitrary floating‐point precision. Based on the experimental data we then model the impact of floating‐point pipeline precision, sampling frequency and fixed‐point input data quantization on the fidelity of rendered images. We introduce three models, an average model, which does not adapt to different data nor varying transfer functions, as well as two adaptive models that take the intricacies of a new data set and transfer function into account by adapting themselves given a few different images rendered. We also test and validate our models based on new data that was not used during our model building.

This paper presents a novel technique to efficiently compute illumination for Direct Volume Rendering using a local approximation of ambient occlusion to integrate the intensity of incident light for each voxel. An advantage with this local approach is that fully shadowed regions are avoided, a desirable feature in many applications of volume rendering such as medical visualization. Additional transfer function interactions are also presented, for instance, to highlight specific structures with luminous tissue effects and create an improved context for semitransparent tissues with a separate absorption control for the illumination settings. Multiresolution volume management and GPU-based computation are used to accelerate the calculations and support large data sets. The scheme yields interactive frame rates with an adaptive sampling approach for incrementally refined illumination under arbitrary transfer function changes. The illumination effects can give a better understanding of the shape and density of tissues and so has the potential to increase the diagnostic value of medical volume rendering. Since the proposed method is gradient-free, it is especially beneficial at the borders of clip planes, where gradients are undefined, and for noisy data sets.

In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patient's symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the user's specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.

Abstract Multiple‐volume visualization is a growing field in medical imaging providing simultaneous exploration of volumes acquired from varying modalities. However, high complexity results in an increased strain on performance compared to single volume rendering as scenes may consist of volumes with arbitrary orientations and rendering is performed with varying sample densities. Expensive image order techniques such as depth peeling have previously been used to perform the necessary calculations. In this work we present a view‐independentregion based scene descriptionfor multi‐volume pipelines. Using Binary Space Partitioning we are able to create a simple interface providing all required information for advanced multi‐volume renderings while introducing a minimal overhead for scenes with few volumes. The modularity of our solution is demonstrated by the use of visual development and performance is documented with benchmarks and real‐time simulations.

In this paper we present novel means to communicate and present gained knowledge using semi-guided interactive visualizations embedded in standard HTML documents. The goal of the work is to let the analyst (author) explore data and simultaneously save important discoveries and thus enable collaboration and sharing of gained insights over the Internet. The readers of the document will benefit from receiving an integrated and interactive document that can "coach" them in the understanding and testing of hypotheses leading to faster understanding and a higher confidence level in the presented visual information. The approach is based on the Visual Analytics (VA) toolkit (GAV), which also provides a mechanism that supports the storage of interactive events in an analytical reasoning process through "memorized interactive visualization views" or snapshots that can be saved at any time during an explorative data analysis process. In a typical scenario an application, comprised of GAV VA components, is embedded in the HTML document together with target data and the snapshots of discoveries. Any standard HTML event, such as the user clicking a textual link or an image can be used to instantiate a snapshot, creating a link between a descriptive text and an interactive visualization presentation. As the GAV application and corresponding data are integrated with the HTML code, residing on the client machine, hardware accelerated graphics can be used to provide interactive performance of demanding visualizations. We demonstrate the potential of the developed methods in the context of GeoAnalytics and molecular visualization scenarios.

Abstract This paper presents methods for photo‐realistic rendering using strongly spatially variant illumination captured from real scenes. The illumination is captured along arbitrary paths in space using a high dynamic range, HDR, video camera system with position tracking. Light samples are rearranged into 4‐D incident light fields (ILF) suitable for direct use as illumination in renderings. Analysis of the captured data allows for estimation of the shape, position and spatial and angular properties of light sources in the scene. The estimated light sources can be extracted from the large 4D data set and handled separately to render scenes more efficiently and with higher quality. The ILF lighting can also be edited for detailed artistic control.

The three papers in this special section are extended versions of three papers from the Ninth Eurographics/IEEE VGTC Symposium on Visualization (EuroVis '07), held in Norrkoping, Sweden, May 23-25, 2007. The papers are summarized here.

With current methods for volume haptics in scientific visualization, features in time-varying data can freely move straight through the haptic probe without generating any haptic feedback the algorithms are simply not designed to handle variation with time but consider only the instantaneous configuration when the haptic feedback is calculated. This article introduces haptic rendering of dynamic volumetric data to provide a means for haptic exploration of dynamic behaviour in volumetric data. We show how haptic feedback can be produced that is consistent with volumetric data moving within the virtual environment and with data that, in itself, evolves over time. Haptic interaction with time-varying data is demonstrated by allowing palpation of a CT sequence of a beating human heart.

In this paper we present an in situ evaluation of a haptic system, with a representative test population, we aim to determine what, if any, benefit haptics can have in a biomolecular education context. We have developed a haptic application for conveying concepts of molecular interactions, specifically in protein-ligand docking. Utilizing a semi-immersive environment with stereo graphics, users are able to manipulate the ligand and feel its interactions in the docking process. The evaluation used cognitive knowledge tests and interviews focused on learning gains. Compared with using time efficiency as the single quality measure this gives a better indication of a system's applicability in an educational environment. Surveys were used to gather opinions and suggestions for improvements. Students do gain from using the application in the learning process but the learning appears to be independent of the addition of haptic feedback. However the addition of force feedback did decrease time requirements and improved the students understanding of the docking process in terms of the forces involved, as is apparent from the students' descriptions of the experience. The students also indicated a number of features which could be improved in future development

Direct Volume Rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.

We have developed a prototype system for visual inspection of hidden structures using a mobile phone wireless ZigBee sensor network. Data collected from an embedded wireless sensor matrix is used to synthesize graphics in real-time. Combining this with augmented reality technology on a mobile phone yields a novel approach to on-site inspection of a broad range of elements and their current internal states.

We present a direct interblock interpolation technique that enables direct volume rendering of blocked, multiresolution volumes. The proposed method smoothly interpolates between blocks of arbitrary block-wise level-of-detail (LOD) without sample replication or padding. This permits extreme changes in resolution across block boundaries and removes the interblock dependency for the LOD creation process. In addition the full data reduction from the LOD selection can be maintained throughout the rendering pipeline. Our rendering pipeline employs a flat block subdivision followed by a transfer function based adaptive LOD scheme. We demonstrate the effectiveness of our method by rendering volumes of the order of gigabytes using consumer graphics cards on desktop PC systems.

The high complexity of Transfer Function (TF) design is a major obstacle to widespread routine use of Direct Volume Rendering, particularly in the case of medical imaging. Both manual and automatic TF design schemes would benefit greatly from a fast and simple method for detection of tissue value ranges. To this end, we introduce the a-histogram, an enhancement that amplifies ranges corresponding to spatially coherent materials. The properties of the a-histogram have been explored for synthetic data sets and then successfully used to detect vessels in 20 Magnetic Resonance angiographies, proving the potential of this approach as a fast and simple technique for histogram enhancement in general and for TF construction in particular.

This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolution data from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added value and future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands of the procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsies involving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using level-of-detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large, out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of the pipeline are presented using data sets from forensic cases.

Direct Volume Rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current Transfer Function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce Partial Range Histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF.

Direct Volume Rendering (DVR) is known to be of diagnostic value in the analysis of medical data sets. However, its deployment in everyday clinical use has so far been limited. Two major challenges are that the current methods for Transfer Function (TF) construction are too complex and that the tissue separation abilities of the TF need to be extended. In this paper we propose the use of histogram analysis in local neighborhoods to address both these conflicting problems. To reduce TF construction difficulty, we introduce Partial Range Histograms in an automatic tissue detection scheme, which in connection with Adaptive Trapezoids enable efficient TF design. To separate tissues with overlapping intensity ranges, we propose a fuzzy classification based on local histograms as a second TF dimension. This increases the power of the TF, while retaining intuitive presentation and interaction.

When planning a Leksell GammaKnife/spl reg/ treatment, dose planners place iso-centres of the irradiation field in such a way that a certain iso-dose surface conforms as closely as possible to a target, such as a tumour. Today this planning is done primarily in 2D, and the clinician places the iso-centres onto the current medical image. The images used are usually acquired by MRl, CT or angiography. In This work we investigate the use of interactive 3D visualization and haptics to perform the dose planning. In a pilot implementation we provide the user with a virtual environment with real-time graphics to visualize the target, surrounding tissue and iso-dose surfaces as well as an integrated graphical user interface. The overall aim of the implementation is to increase the efficiency and precision of the planning process by adding haptic feedback to represent various constraints and opportunities in the planning process and so guide the user to an optimal placement of the iso-centres. Preliminary feedback from potential users has been very positive.

The authors present a visualization system for interactive real time animation and visualization of simulation results from a parallel Particle-in-Cell code. The system was designed and implemented for the Onyx2 Infinite Reality hardware. A number of different visual objects, such as volume rendered particle density functionals were implemented. To provide sufficient frame rates for interactive visualization, the system was designed to provide performance close to the hardware specifications both in terms of the I/O and graphics subsystems. The presented case study applies the developed system to the evolution of an instability that gives rise to a plasma surfatron, a mechanism which rapidly can accelerate particles to very high velocities and thus be of great importance in the context of electron acceleration in astrophysical shocks, in the solar corona and in particle accelerators. The produced visualizations have allowed us to identify a previously unknown saturation mechanism for the surfatron and direct research efforts into new areas of interest.