This work reports how text size and other rendering conditions affect reading speeds in a virtual reality environment and a scientific data analysis application. Displaying text legibly yet space-efficiently is a challenging problem in immersive displays. Effective text displays that enable users to read at their maximum speed must consider the variety of virtual reality (VR) display hardware and possible visual exploration tasks. We investigate how text size and display parameters affect reading speed and legibility in three state-of-the-art VR displays: two head-mounted displays and one CAVE. In our perception experiments, we establish limits where reading speed declines as the text size approaches the so-called critical print sizes (CPS) of individual displays, which can inform the design of uniform reading experiences across different VR systems. We observe an inverse correlation between display resolution and CPS. Yet, even in high-fidelity VR systems, the measured CPS was larger than in comparable physical text displays, highlighting the value of increased VR display resolutions in certain visualization scenarios. Our findings indicate that CPS can be an effective metric for evaluating VR display usability. Additionally, we evaluate the effects of text panel placement, orientation, and occlusion-reducing rendering methods on reading speeds in generic volumetric particle visualizations. Our study provides insights into the trade-off between text representation and legibility in cluttered immersive environments with specific suggestions for visualization designers and highlight areas for further research.

How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation—via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, finding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies potential features that could be investigated in human correlation perception studies. Together, this paper suggests that deep neural networks can serve as a tool for visualization perception researchers in devising potential empirical study designs and hypothesizing about perpetual judgments. The preprint, data, code, and training logs are available at https://doi.org/10.17605/osf.io/exa8m.

Immersive visualization in virtual reality (VR) allows us to exploit visual cues for perception in 3D space, yet few existing studies have measured the effects of visual cues. Across a desktop monitor and a head-mounted display (HMD), we assessed scatterplot designs which vary their use of visual cues-motion, shading, perspective (graphical projection), and dimensionality-on two sets of data. We conducted a user study with a summary task in which 32 participants estimated the classification accuracy of an artificial neural network from the scatterplots. With Bayesian multilevel modeling, we capture the intricate visual effects and find that no cue alone explains all the variance in estimation error. Visual motion cues generally reduce participants' estimation error; besides this motion, using other cues may increase participants' estimation error. Using an HMD, adding visual motion cues, providing a third data dimension, or showing a more complicated dataset leads to longer response times. We speculate that most visual cues may not strongly affect perception in immersive analytics unless they change people's mental model about data. In summary, by studying participants as they interpret the output from a complicated machine learning model, we advance our understanding of how to use the visual cues in immersive analytics.

Stress tensor fields play a central role in solid mechanics studies, but their visualization in 3D space remains challenging as the information-dense multi-variate tensor needs to be sampled in 3D space while avoiding clutter. Taking cues from current tensor visualizations, we adapted glyph-based visualization for stress tensors in 3D space. We also developed a testing framework and performed user studies to evaluate the various glyph-based tensor visualizations for objective accuracy measures, and subjective user feedback for each visualization method. To represent the stress tensor, we color encoded the original superquadric glyph, and in the user study, we compared it to superquadric glyphs developed for second-order symmetric tensors. We found that color encoding improved the user accuracy measures, while the users also rated our method the highest. We compared our method of placing stress tensor glyphs on displacement streamlines to the glyph placement on a 3D grid. In the visualization, we modified the glyph to show both the stress tensor and the displacement vector at each sample point. The participants preferred our method of glyph placement on displacement streamlines as it highlighted the underlying continuous structure in the tensor field.

We present exploratory research of virtual reality techniques and mnemonic devices to assist in retrieving knowledge from scholarly articles. We used abstracts of scientific publications to represent knowledge in scholarly articles; participants were asked to read, remember, and retrieve knowledge from a set of abstracts. We conducted an experiment to compare participants' recall and recognition performance in three different conditions: a control condition without a pre-specified strategy to test baseline individual memory ability, a condition using an image-based variant of a mnemonic called a "memory palace," and a condition using a virtual reality-based variant of a memory palace. Our analyses show that using a virtual reality-based memory palace variant greatly increased the amount of knowledge retrieved and retained over the baseline, and it shows a moderate improvement over the other image-based memory palace variant. Anecdotal feedback from participants suggested that personalizing a memory palace variant would be appreciated. Our results support the value of virtual reality for some high-level cognitive tasks and help improve future applications of virtual reality and visualization.

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) spherical colormaps for tract tracing tasks (uniform gray, absolute, eigenmaps, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow colormap led to the same ensemble mean accuracy as other colormaps. However, more than 50 percent of the answers consistently had higher estimates of the ensemble average, independent of the mean values. The number of hues, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that both Boy's surface embedding (greatest spatial resolution and contrast) and absolute colormaps (lowest spatial resolution and contrast) led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. Absolute colormap broadly used in brain science is a good default spherical colormap. We could conclude from our study that human visual processing of a chunk of colors differs from that of single colors.

This work analyzes sensemaking frameworks and experiments with an iteratively designed visual analysis tool to identify design implications for facilitating research idea generation using visualizations. Our tool, ThoughtFlow, structures and visualizes literature collections using topic models to bridge the information gap between core activities during research ideation. To help users stay focused on a topic while discovering relevant documents, we designed and analyzed usage patterns for two types of embedded visualization that help determine document relevance while minimizing distraction. We analyzed how research ideation outcomes and processes differ when using ThoughtFlow and conventional search engines by augmenting insight-based evaluation with concept-map analysis. Our results suggest that operations afforded by topic models match well with later ideation stages when coherent topics have emerged, but not with early stages when users are still relying heavily on individual keywords to gather background knowledge. We also present qualitative evidence that citation sparklines encourage more exploration of recommended references, and that a preference for paper thumbnails may depend on the consistency between the evidence and the current mental frame.

We present the results of a two-year design study to developing virtual reality (VR) flow visualization tools for the analysis of dinosaur track creation in a malleable substrate. Using Scientific Sketching methodology, we combined input from illustration artists, visualization experts, and domain scientists to create novel visualization methods. By iteratively improving visualization concepts at multiple levels of abstraction we helped domain scientists to gain insights into the relationship between dinosaur foot movements and substrate deformations. We involved over 20 art and computer science students from a VR design course in a rapid visualization sketching cycle, guided by our paleontologist collaborators through multiple critique sessions. This allowed us to explore a wide range of potential visualization methods and select the most promising methods for actual implementation. Our resulting visualization methods provide paleontologists with effective tools to analyze their data through particle, pathline and time surface visualizations. We also introduce a set of visual metaphors to compare foot motion in relation to substrate deformation by using pathsurfaces. This is one of the first large-scale projects using Scientific Sketching as a development methodology. We discuss how the research questions of our collaborators have evolved during the sketching and prototyping phases. Finally, we provide lessons learned and usage considerations for Scientific Sketching based on the experiences gathered during this project.

Objective was to assess the relationship between task demand, mental effort, task difficulty, and performance during physicians’ interaction with electronic health records (EHRs). Seventeen physicians performed three EHR-based scenarios with varying task demands. Mental effort was measured using eye tracking measures via task evoked pupillary responses (TEPR), blink frequency, and gaze speed; task difficulty (or user behavior) was measured using frequent mouse click patterns and task flow; user performance was quantified using two types of omission errors: (i) omission errors with no evidence of trying to complete the task and (ii) omission error with evidence of trying but unable to complete the task. The results indicated that task demand significantly increased mental effort, but not task difficulty. Task demand, mental effort, and task difficulty all predicted performance. Specifically, there was a significant relationship between (i) task demand, TEPR and omission errors with no evidence of trying to complete the task, and (ii) blink frequency, repeated search clicks and omission error with evidence of trying but unable to complete the task. In concert, results suggest that physicians’ performance during EHR interaction was negatively affected by task demands and increase in mental effort. This highlight the need for implementation of appropriate quality assurance (QA) measures, in addition to EHR usability improvement, to minimize omission errors and improve physician’s performance. Additionally, the lack of relationship between task demand and task difficulty highlights a need for further methodological and empirical studies to advance our understanding from theory to application during physician–EHR interaction.

We show how mouse interaction log classification can help visualization toolsmiths understand how their tools are used "in the wild" through an evaluation of MAGI - a cancer genomics visualization tool. Our primary contribution is an evaluation of twelve visual analysis task classifiers, which compares predictions to task inferences made by pairs of genomics and visualization experts. Our evaluation uses common classifiers that are accessible to most visualization evaluators: -nearest neighbors, linear support vector machines, and random forests. By comparing classifier predictions to visual analysis task inferences made by experts, we show that simple automated task classification can have up to 73 percent accuracy and can separate meaningful logs from "junk" logs with up to 91 percent accuracy. Our second contribution is an exploration of common MAGI interaction trends using classification predictions, which expands current knowledge about ecological cancer genomics visualization tasks. Our third contribution is a discussion of how automated task classification can inform iterative tool design. These contributions suggest that mouse interaction log analysis is a viable method for (1) evaluating task requirements of client-side-focused tools, (2) allowing researchers to study experts on larger scales than is typically possible with in-lab observation, and (3) highlighting potential tool evaluation bias.

We present the design and evaluation of a method for estimating gaze locations during the analysis of static visualizations using crowdsourcing. Understanding gaze patterns is helpful for evaluating visualizations and user behaviors, but traditional eye-tracking studies require specialized hardware and local users. To avoid these constraints, we developed a method called Fauxvea, which crowdsources visualization tasks on the Web and estimates gaze fixations through cursor interactions without eye-tracking hardware. We ran experiments to evaluate how gaze estimates from our method compare with eye-tracking data. First, we evaluated crowdsourced estimates for three common types of information visualizations and basic visualization tasks using Amazon Mechanical Turk (MTurk). In another, we reproduced findings from a previous eye-tracking study on tree layouts using our method on MTurk. Results from these experiments show that fixation estimates using Fauxvea are qualitatively and quantitatively similar to eye tracking on the same stimulus-task pairs. These findings suggest that crowdsourcing visual analysis tasks with static information visualizations could be a viable alternative to traditional eye-tracking studies for visualization research and design.

We present an evaluation of Colorgorical, a web-based tool for creating discriminable and aesthetically preferable categorical color palettes. Colorgorical uses iterative semi-random sampling to pick colors from CIELAB space based on user-defined discriminability and preference importances. Colors are selected by assigning each a weighted sum score that applies the user-defined importances to Perceptual Distance, Name Difference, Name Uniqueness, and Pair Preference scoring functions, which compare a potential sample to already-picked palette colors. After, a color is added to the palette by randomly sampling from the highest scoring palettes. Users can also specify hue ranges or build off their own starting palettes. This procedure differs from previous approaches that do not allow customization (e.g., pre-made ColorBrewer palettes) or do not consider visualization design constraints (e.g., Adobe Color and ACE). In a Palette Score Evaluation, we verified that each scoring function measured different color information. Experiment 1 demonstrated that slider manipulation generates palettes that are consistent with the expected balance of discriminability and aesthetic preference for 3-, 5-, and 8-color palettes, and also shows that the number of colors may change the effectiveness of pair-based discriminability and preference scores. For instance, if the Pair Preference slider were upweighted, users would judge the palettes as more preferable on average. Experiment 2 compared Colorgorical palettes to benchmark palettes (ColorBrewer, Microsoft, Tableau, Random). Colorgorical palettes are as discriminable and are at least as preferable or more preferable than the alternative palette sets. In sum, Colorgorical allows users to make customized color palettes that are, on average, as effective as current industry standards by balancing the importance of discriminability and aesthetic preference.

We report the findings of a study designed to evaluate the effect of stereopsis and field of regard (FOR) in two different mixed reality (MR) simulation platforms: a head-mounted display (HMD) and a CAVE. We compared the performance of participants on two levels of stereopsis (mono and stereo) and two levels of FOR (90 degrees and 270 degrees) using a variety of scientific visualization tasks. Among the findings, we observed that not all the effects were consistent between the platforms. Stereo alone or in combination with higher FOR improved completion time on both platforms. However, adding stereo solely reduced the accuracy of the participants on the CAVE and improved on the HMD. Our findings extend prior knowledge on the contribution of visual fidelity components and suggests potential limits on MR simulation between platforms.

We present results from an experiment aimed at using logs of interactions with a visual analytics application to better understand how interactions lead to insight generation. We performed an insight-based user study of a visual analytics application and ran post hoc quantitative analyses of participants' measured insight metrics and interaction logs. The quantitative analyses identified features of interaction that were correlated with insight characteristics, and we confirmed these findings using a qualitative analysis of video captured during the user study. Results of the experiment include design guidelines for the visual analytics application aimed at supporting insight generation. Furthermore, we demonstrated an analysis method using interaction logs that identified which interaction patterns led to insights, going beyond insight-based evaluations that only quantify insight characteristics. We also discuss choices and pitfalls encountered when applying this analysis method, such as the benefits and costs of applying an abstraction framework to application-specific actions before further analysis. Our method can be applied to evaluations of other visualization tools to inform the design of insight-promoting interactions and to better understand analyst behaviors.

When visualizing data with uncertainty, a common approach is to treat uncertainty as an additional dimension and encode it using a visual variable. The effectiveness of this approach depends on how the visual variables chosen for representing uncertainty and other attributes interact to influence the user's perception of each variable. We report a user study on the perception of graph edge attributes when uncertainty associated with each edge and the main edge attribute are visualized simultaneously using two separate visual variables. The study covers four visual variables that are commonly used for visualizing uncertainty on line graphical primitives: lightness, grain, fuzziness, and transparency. We select width, hue, and saturation for visualizing the main edge attribute and hypothesize that we can observe interference between the visual variable chosen to encode the main edge attribute and that to encode uncertainty, as suggested by the concept of dimensional integrality. Grouping the seven visual variables as color-based, focus-based, or geometry-based, we further hypothesize that the degree of interference is affected by the groups to which the two visual variables belong. We consider two further factors in the study: discriminability level for each visual variable as a factor intrinsic to the visual variables and graph-task type (visual search versus comparison) as a factor extrinsic to the visual variables. Our results show that the effectiveness of a visual variable in depicting uncertainty is strongly mediated by all the factors examined here. Focus-based visual variables (fuzziness, grain, and transparency) are robust to the choice of visual variables for encoding the main edge attribute, though fuzziness has stronger negative impact on the perception of width and transparency has stronger negative impact on the perception of hue than the other uncertainty visual variables. We found that interference between hue and lightness is much greater than that between saturation and lightness, though all three are color-based visual variables. We also found a compound relationship between discriminability level and the degree of dimensional integrality. We discuss the generalizability and limitation of the results and conclude with design considerations for visualizing graph uncertainty derived from these results, including recommended choices of visual variables when the relative importance of data attributes and graph tasks is known.

We present the design of Brown University's new Cave, which is expected to be fully operational in February 2014. With one arc-minute resolution, 3.8 π steradians of visual surround, head-tracked stereo, and an almost seamless screen, this Cave offers advances to the state-of-the-art virtual reality experience. This improvement is achieved with the installation of 69 high-resolution long throw projectors, a cylindrical screen with conical ceiling, and a 135 square foot rear-projection floor. Though Caves have been around for over 20 years, they have remained impractical for many potential uses due to their limited resolution, brightness, and overall immersion. Brown's new Cave aims to bridge this gap.

In this paper we make the following contributions: (1) we describe how the grouping, quantity, and size of visual marks affects search time based on the results from two experiments; (2) we report how search performance relates to self-reported difficulty in finding the target for different display types; and (3) we present design guidelines based on our findings to facilitate the design of effective visualizations. Both Experiment 1 and 2 asked participants to search for a unique target in colored visualizations to test how the grouping, quantity, and size of marks affects user performance. In Experiment 1, the target square was embedded in a grid of squares and in Experiment 2 the target was a point in a scatterplot. Search performance was faster when colors were spatially grouped than when they were randomly arranged. The quantity of marks had little effect on search time for grouped displays ("pop-out"), but increasing the quantity of marks slowed reaction time for random displays. Regardless of color layout (grouped vs. random), response times were slowest for the smallest mark size and decreased as mark size increased to a point, after which response times plateaued. In addition to these two experiments we also include potential application areas, as well as results from a small case study where we report preliminary findings that size may affect how users infer how visualizations should be used. We conclude with a list of design guidelines that focus on how to best create visualizations based on grouping, quantity, and size of visual marks.

We present results from a user study of task performance on streamtube visualizations, such as those used in three-dimensional (3D) vector and tensor field visualizations. This study used a tensor field sampled from a full-brain diffusion tensor magnetic resonance imaging (DTI) dataset. The independent variables include illumination model (global illumination and OpenGL-style local illumination), texture (with and without), motion (with and without), and task. The three spatial analysis tasks are: (1) a depth-judgment task: determining which of two marked tubes is closer to the user's viewpoint, (2) a visual-tracing task: marking the endpoint of a tube, and (3) a contact-judgment task: analyzing tube-sphere penetration. Our results indicate that global illumination did not improve task completion time for the tasks we measured. Global illumination reduced the errors in participants' answers over local OpenGLstyle rendering for the visual-tracing task only when motion was present. Motion contributed to spatial understanding for all tasks, but at the cost of longer task completion time. A high-frequency texture pattern led to longer task completion times and higher error rates. These results can help in the design of lighting model, such as flow or diffusion-tensor field visualizations and identify situations when the lighting is more efficient and accurate.

We present TOME, a novel framework that helps developers quantitatively evaluate user interfaces and design iterations by using histories from crowds of end users. TOME collects user-interaction histories via an interface instrumentation library as end users complete tasks; these histories are compiled using the Keystroke-Level Model (KLM) into task completion-time predictions using CogTool. With many histories, TOME can model prevailing strategies for tasks without needing an HCI specialist to describe users' interaction steps. An unimplemented design change can be evaluated by perturbing a TOME task model in CogTool to reflect the change, giving a new performance prediction. We found that predictions for quick (5-60s) query tasks in an instrumented brain-map interface averaged within 10% of measured expert times. Finally, we modified a TOME model to predict closely the speed-up yielded by a proposed interaction before implementing it.

Subtle changes in analysis system interfaces can be used purposely to alter users' analytic behaviors. In a controlled study subjects completed three analyses at one-week intervals using an analysis support system. Control subjects used one interface in all sessions. Test subjects used modified versions in the last two sessions: a first set of changes aimed at increasing subjects' use of the system and their consideration of alternative hypotheses; a second set of changes aimed at increasing the amount of evidence collected. Results show that in the second session test subjects used the interface 39% more and switched between hypotheses 19% more than in the first session. They then collected 26% more evidence in the third than in the second session. These increases differ significantly (p<0.05) from near constant control rates. We hypothesize that this approach can be used in many real applications to guide analysts unobtrusively towards improved analytic strategies.

We present an analysis of two user strategies in interactive data analysis, based on an observational study of four researchers in the immunology domain. Screen captures, video records, interviews, and verbal protocols are used to analyze common procedures in this type of visual data analysis, as well as how these procedures differ among these users. Our findings present a case where skilled users can approach a similar problem with diverging analysis strategies. In the group we observed, strategies fell within two broad categories: within-graph analysis, in which a user generates a few graph layouts and interacts heavily within them, and between-graph analysis, in which a user generates a series of graphs and switches between them in sequence. Differences in strategies lead to distinct interaction patterns, and are likely to be best supported by different interface designs. We characterize these observed strategies and discuss their implications for scientific visualization design and evaluation.

We report the impact of display characteristics (stereo and size) on task performance in diffusion magnetic resonance imaging (DMRI) in a user study with 12 participants. The hypotheses were that (1) adding stereo and increasing display size would improve task accuracy and reduce completion time, and (2) the greater the complexity of a spatial task, the greater the benefits of an improved display. Thus we expected to see greater performance gains when detailed visual reasoning was required. Participants used dense streamtube visualizations to perform five representative tasks: (1) determine the higher average fractional anisotropy (FA) values between two regions, (2) find the endpoints of fiber tracts, (3) name a bundle, (4) mark a brain lesion, and (5) judge if tracts belong to the same bundle. Contrary to our hypotheses, we found the task completion time was not improved by the use of the larger display and that performance accuracy was hurt rather than helped by the introduction of stereo in our study with dense DMRI data. Bigger was not always better. Thus cautious should be taken when selecting displays for scientific visualization applications. We explored the results further using the body-scale unit and subjective size and stereo experiences.

We present an ethnographic study of design differences in visual presentations between academic disciplines. Characterizing design conventions between users and data domains is an important step in developing hypotheses, tools, and design guidelines for information visualization. In this paper, disciplines are compared at a coarse scale between four groups of fields: social, natural, and formal sciences; and the humanities. Two commonplace presentation types were analyzed: electronic slideshows and whiteboard "chalk talks". We found design differences in slideshows using two methods - coding and comparing manually-selected features, like charts and diagrams, and an image-based analysis using PCA called eigenslides. In whiteboard talks with controlled topics, we observed design behaviors, including using representations and formalisms from a participant's own discipline, that suggest authors might benefit from novel assistive tools for designing presentations. Based on these findings, we discuss opportunities for visualization ethnography and human-centered authoring tools for visual information.

We introduce two-dimensional neural maps for exploring connectivity in the brain. For this, we create standard streamtube models from diffusion-weighted brain imaging data sets along with neural paths hierarchically projected into the plane. These planar neural maps combine desirable properties of low-dimensional representations, such as visual clarity and ease of tract-of-interest selection, with the anatomical familiarity of 3D brain models and planar sectional views. We distribute this type of visualization both in a traditional stand-alone interactive application and as a novel, lightweight web-accessible system. The web interface integrates precomputed neural-path representations into a geographical digital-maps framework with associated labels, metrics, statistics, and linkouts. Anecdotal and quantitative comparisons of the present method with a recently proposed 2D point representation suggest that our representation is more intuitive and easier to use and learn. Similarly, users are faster and more accurate in selecting bundles using the 2D path representation than the 2D point representation. Finally, expert feedback on the web interface suggests that it can be useful for collaboration as well as quick exploration of data.

No abstract available.

The six papers in this special issue are extended versions of papers presented at the IEEE/EG International Symposium om Volume Graphics, held in September 2007 in Prague, and the joint event of the IEEE/EG International Symposia on Volume Graphics (VG '08) and Point-Based Graphics (PBG '08), held in August 2008 in Los Angeles.

We introduce several novel visualization and interaction paradigms for visual analysis of published protein-protein interaction networks, canonical signaling pathway models, and quantitative proteomic data. We evaluate them anecdotally with domain scientists to demonstrate their ability to accelerate the proteomic analysis process. Our results suggest that structuring protein interaction networks around canonical signaling pathway models, exploring pathways globally and locally at the same time, and driving the analysis primarily by the experimental data, all accelerate the understanding of protein pathways. Concrete proteomic discoveries within T-cells, mast cells, and the insulin signaling pathway validate the findings. The aim of the paper is to introduce novel protein network visualization paradigms and anecdotally assess the opportunity of incorporating them into established proteomic applications. We also make available a prototype implementation of our methods, to be used and evaluated by the proteomic community.

In this work we present, apply, and evaluate a novel, interactive visualization model for comparative analysis of structural variants and rearrangements in human and cancer genomes, with emphasis on data integration and uncertainty visualization. To support both global trend analysis and local feature detection, this model enables explorations continuously scaled from the high-level, complete genome perspective, down to the low-level, structural rearrangement view, while preserving global context at all times. We have implemented these techniques in Gremlin, a genomic rearrangement explorer with multi-scale, linked interactions, which we apply to four human cancer genome data sets for evaluation. Using an insight-based evaluation methodology, we compare Gremlin to Circos, the state-of-the-art in genomic rearrangement visualization, through a small user study with computational biologists working in rearrangement analysis. Results from user study evaluations demonstrate that this visualization model enables more total insights, more insights per minute, and more complex insights than the current state-of-the-art for visual analysis and exploration of genome rearrangements.

We introduce the concept of coloring close and crossing edges in graph drawings with perceptually opposing colors making them individually more distinguishable and reducing edge-crossing effects. We define a "closeness" metric on edges as a combination of distance, angle and crossing. We use the inverse of this metric to compute a color embedding in the L*a*b* color space and assign "close" edges colors that are perceptually far apart. We present the following results: a distance metric on graph edges, a method of coloring graph edges, and anecdotal evidence that this technique can improve the reading of graph edges.

We introduce a new method for coloring 3D line fields and show results from its application in visualizing orientation in DTI brain data sets. The method uses Boy's surface, an immersion of RP2 in 3D. This coloring method is smooth and one-to-one except on a set of measure zero, the double curve of Boy's surface.

In a user study comparing four visualization methods for three-dimensional vector data, participants used visualizations from each method to perform five simple but representative tasks: 1) determining whether a given point was a critical point, 2) determining the type of a critical point, 3) determining whether an integral curve would advect through two points, 4) determining whether swirling movement is present at a point, and 5) determining whether the vector field is moving faster at one point than another. The visualization methods were line and tube representations of integral curves with both monoscopic and stereoscopic viewing. While participants reported a preference for stereo lines, quantitative results showed performance among the tasks varied by method. Users performed all tasks better with methods that: 1) gave a clear representation with no perceived occlusion, 2) clearly visualized curve speed and direction information, and 3) provided fewer rich 3D cues (e.g., shading, polygonal arrows, overlap cues, and surface textures). These results provide quantitative support for anecdotal evidence on visualization methods. The tasks and testing framework also give a basis for comparing other visualization methods, for creating more effective methods, and for defining additional tasks to explore further the tradeoffs among the methods.

We present a visual exploration paradigm that facilitates navigation through complex fiber tracts by combining traditional 3D model viewing with lower dimensional representations. To this end, we create standard streamtube models along with two two-dimensional representations, an embedding in the plane and a hierarchical clustering tree, for a given set of fiber tracts. We then link these three representations using both interaction and color obtained by embedding fiber tracts into a perceptually uniform color space. We describe an anecdotal evaluation with neuroscientists to assess the usefulness of our method in exploring anatomical and functional structures in the brain. Expert feedback indicates that, while a standalone clinical use of the proposed method would require anatomical landmarks in the lower dimensional representations, the approach would be particularly useful in accelerating tract bundle selection. Results also suggest that combining traditional 3D model viewing with lower dimensional representations can ease navigation through the complex fiber tract models, improving exploration of the connectivity in the brain.

Abstract We present novel visual and interactive techniques for exploratory visualization of animal kinematics using instantaneous helical axes (IHAs). The helical axis has been used in orthopedics, biomechanics, and structural mechanics as a construct for describing rigid body motion. Within biomechanics, recent imaging advances have made possible accurate high‐speed measurements of individual bone positions and orientations during experiments. From this high‐speed data, instantaneous helical axes of motion may be calculated. We address questions of effective interactive, exploratory visualization of this high‐speed 3D motion data. A 3D glyph that encodes all parameters of the IHA in visual form is presented. Interactive controls are used to examine the change in the IHA over time and relate the IHA to anatomical features of interest selected by a user. The techniques developed are applied to a stereoscopic, interactive visualization of the mechanics of pig mastication and assessed by a team of evolutionary biologists who found interactive IHA‐based analysis a useful addition to more traditional motion analysis techniques.

We describe an experiment in which art and illustration experts evaluated six 2D vector visualization methods. We found that these expert critiques mirrored previously recorded experimental results; these findings support that using artists, visual designers and illustrators to critique scientific visualizations can be faster and more productive than quantitative user studies. Our participants successfully evaluated how well the given methods would let users complete a given set of tasks. Our results show a statistically significant correlation with a previous objective study: designers' subjective predictions of user performance by these methods match users measured performance. The experts improved the evaluation by providing insights into the reasons for the effectiveness of each visualization method and suggesting specific improvements.

We present four studies investigating tools and methodologies for artist-scientist-technologist collaboration in designing multivariate, virtual reality (VR) visualizations. Design study 1 identifies the promise of 3D drawing-style interfaces for VR design and also establishes limitations of these tools with respect to precision and support for animation. Design study 2 explores animating artist-created visualization designs with scientific 3D fluid flow data. While results captured an accurate sense of flow that was advantageous as compared to the results of study 1, the potential for visual exploration using the design tools tested was limited. Design study 3 reveals the importance of a new 3D interface that overcomes the precision limitation found in study 1 while remaining accessible to artist collaborators. Drawing upon previous results, design study 4 engages collaborative teams in a design process that begins with traditional paper sketching and moves to animated, interactive, VR prototypes "sketched" by designers in VR using interactive 3D tools. Conclusions from these four studies identify important characteristics of effective artist-accessible VR visualization design tools and lead to a proposed formalized methodology for successful collaborative design that we expect to be useful in guiding future collaborations. We call this proposed methodology Scientific Sketching.

We present a method for clustering diffusion tensor imaging (DTI) integral curves into anatomically plausible bundles. An expert rater evaluated the anatomical accuracy of the bundles. We also evaluated the method by applying an experimental cross-subject labeling method to the clustering results. We first employ a sampling and culling strategy for generating DTI integral curves and then constrain the curves so that they terminate in gray matter. We then employ a clustering method based on a proximity measure calculated between every pair of curves. We interactively selected a proximity threshold to achieve visually optimal clustering in models from four DTI datasets. An expert rater then assigned a confidence rating about bundle presence and accuracy for each of 12 target fiber bundles of varying calibers and type in each dataset. We then created a fiber bundle template to cluster and label the fiber bundles automatically in new datasets. According to expert evaluation, the automated proximity-based clustering and labeling algorithm consistently yields anatomically plausible fiber bundles on large and coherent clusters. This work has the potential to provide an automatic and robust way to find and study neural fiber bundles within DTI.

We present Drawing on Air, a haptic-aided input technique for drawing controlled 3D curves through space. Drawing on Air addresses a control problem with current 3D modeling approaches based on sweeping movement of the hands through the air. While artists praise the immediacy and intuitiveness of these systems, a lack of control makes it nearly impossible to create 3D form beyond quick design sketches or gesture drawings. Drawing on Air introduces two new strategies for more controlled 3D drawing: one-handed drag drawing and two-handed tape drawing. Both approaches have advantages for drawing certain types of curves. We describe a tangent preserving method for transitioning between the two techniques while drawing. Haptic-aided redrawing and line weight adjustment while drawing are also supported in both approaches. In a quantitative user study evaluation by illustrators, the one and two-handed techniques performed at roughly the same level, and both significantly outperformed freehand drawing and freehand drawing augmented with a haptic friction effect. We present the design and results of this experiment as well as user feedback from artists and 3D models created in a style of line illustration for challenging artistic and scientific subjects.

Abstract-Orthopedists invest significant amounts of effort and time trying to understand the biomechanics of arthrodial (gliding) joints. Although new image acquisition and processing methods currently generate richer-than-ever geometry and kinematic data sets that are individual specific, the computational and visualization tools needed to enable the comparative analysis and exploration of these data sets lag behind. In this paper, we present a framework that enables the cross-data-set visual exploration and analysis of arthrodial joint biomechanics. Central to our approach is a computer-vision-inspired markerless method for establishing pairwise correspondences between individual-specific geometry. Manifold models are subsequently defined and deformed from one individual-specific geometry to another such that the markerless correspondences are preserved while minimizing model distortion. The resulting mutually consistent parameterization and visualization allow the users to explore the similarities and differences between two data sets and to define meaningful quantitative measures. We present two applications of this framework to human-wrist data: articular cartilage transfer from cadaver data to in vivo data and cross-data-set kinematics analysis. The method allows our users to combine complementary geometries acquired through different modalities and thus overcome current imaging limitations. The results demonstrate that the technique is useful in the study of normal and injured anatomy and kinematics of arthrodial joints. In principle, the pairwise cross-parameterization method applies to all spherical topology data from the same class and should be particularly beneficial in instances where identifying salient object features is a nontrivial task.

We present a method for registering the position and orientation of bones across multiple computed-tomography (CT) volumes of the same subject. The method is subvoxel accurate, can operate on multiple bones within a set of volumes, and registers bones that have features commensurate in size to the voxel dimension. First, a geometric object model is extracted from a reference volume image. We use then unsupervised tissue classification to generate from each volume to be registered a super-resolution distance field--a scalar field that specifies, at each point, the signed distance from the point to a material boundary. The distance fields and the geometric bone model are finally used to register an object through the sequence of CT images. In the case of multiobject structures, we infer a motion-directed hierarchy from the distance-field information that allows us to register objects that are not within each other's capture region. We describe a validation framework and evaluate the new technique in contrast with grey-value registration. Results on human wrist data show average accuracy improvements of 74% over grey-value registration. The method is of interest to any intrasubject, same-modality registration applications where subvoxel accuracy is desired.

We present an evaluation of a parameterized set of 2D icon-based visualization methods where we quantified how perceptual interactions among visual elements affect effective data exploration. During the experiment, subjects quantified three different design factors for each method: the spatial resolution it could represent, the number of data values it could display at each point, and the degree to which it is visually linear. The class of visualization methods includes Poisson-disk distributed icons where icon size, icon spacing, and icon brightness can be set to a constant or coupled to data values from a 2D scalar field. By only coupling one of those visual components to data, we measured filtering interference for all three design factors. Filtering interference characterizes how different levels of the constant visual elements affect the evaluation of the data-coupled element. Our novel experimental methodology allowed us to generalize this perceptual information, gathered using ad-hoc artificial datasets, onto quantitative rules for visualizing real scientific datasets. This work also provides a framework for evaluating visualizations of multi-valued data that incorporate additional visual cues, such as icon orientation or color.

We present the results from a qualitative and quantitative user study comparing fishtank virtual-reality (VR) and CAVE displays. The results of the qualitative study show that users preferred the fishtank VR display to the CAVE system for our scientific visualization application because of perceived higher resolution, brightness and crispness of imagery, and comfort of use. The results of the quantitative study show that users performed an abstract visual search task significantly more quickly and more accurately on the fishtank VR display system than in the CAVE. The same study also showed that visual context had no significant effect on task performance for either of the platforms. We suggest that fishtank VR displays are more effective than CAVEs for applications in which the task occurs outside the user's reference frame, the user views and manipulates the virtual world from the outside in, and the size of the virtual object that the user interacts with is smaller than the user's body and fits into the fishtank VR display. The results of both studies support this proposition.

We present visualization tools for analyzing molecular simulations of liquid crystal (LC) behavior. The simulation data consists of terabytes of data describing the position and orientation of every molecule in the simulated system over time. Condensed matter physicists study the evolution of topological defects in these data, and our visualization tools focus on that goal. We first convert the discrete simulation data to a sampled version of a continuous second-order tensor field and then use combinations of visualization methods to simultaneously display combinations of contractions of the tensor data, providing an interactive environment for exploring these complicated data. The system, built using AVS, employs colored cutting planes, colored isosurfaces, and colored integral curves to display fields of tensor contractions including Westin's scalar cl, cp, and cs metrics and the principal eigenvector. Our approach has been in active use in the physics lab for over a year. It correctly displays structures already known; it displays the data in a spatially and temporally smoother way than earlier approaches, avoiding confusing grid effects and facilitating the study of multiple time steps; it extends the use of tools developed for visualizing diffusion tensor data, re-interpreting them in the context of molecular simulations; and it has answered long-standing questions regarding the orientation of molecules around defects and the conformational changes of the defects.

We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.

The evolution of computational science over the last decade has resulted in a dramatic increase in raw problem solving capabilities. This growth has given rise to advances in scientific and engineering simulations that have put a high demand on tools for high-performance large-scale data exploration and analysis. These simulations have the potential to generate large amounts of data. Humans, however are relatively poor at gaining insight from raw numerical data, and as a result, have used visualization as a tool for understanding, interpreting and exploring data of all types and sizes. Allowing for efficient visual explorations of data, however, requires that the ratio of knowledge gained versus the cost of the visualization be maximized. This, in turn, mandates the integration of principles from human perception. Understanding perception as it relates to visualization requires that we understand not only the biology of the human visual system, but principles from vision theory, and perceptual psychology as well. This panel is the result of bringing together practioners and researchers from a broad spectrum of interests relating to the ability to maximize the amount of information that is effectively perceived from a given visualization. Position statements will be given by researchers interested in perceptual psychology and the perception of natural images, integrating art and design principles, non-photorealistic rendering techniques, and the use of global illumination methods to provide benefical perceptual cues.

Scientific Visualization (SciVis) has evolved past the point where one undergraduate course can cover all of the necessary topics. So the question becomes "how do we teach SciVis to this generation of students?" Some examples of current courses are: A graduate Computer Science (CS) course that prepares the next generation of SciVis researchers. An undergraduate CS course that prepares the future software architects/developers of packages such as vtk, vis5D and AVS. A class that teaches students how to do SciVis with existing software packages and how to deal with the lack of interoperability between those packages (via either a CS service course or a supercomputing center training course). An inter-disciplinary course designed to prepare computer scientists to work with the "real" scientists (via either a CS or Computational Science course). In this panel, we will discuss these types of courses and the advantages and disadvantages of each. We will also talk about some issues that you have probably encountered at your university: How do we keep the graphics/vis-oriented students from going to industry? How does SciVis fit in with evolving Computational Science programs? Is SciVis destined to be a service course at most universities? How do we deal with the diverse backgrounds of students that need SciVis?

Introduction: We present visualizations that emphasize vortices in simulated airflow around a motion-captured bat model. These visualizations aim to help biologists gain understanding on the mechanics of bat flight. As suggested by our fluid dynamics collaborators, studying the formation and shedding of vortices in the flight of bats will help scientists understand the efficient mechanisms bats employ in generating lift. By understanding bat flight, we hope to make discoveries in areas such as biomechanics, aerodynamics, and evolutionary biology. This work is the time varying extension of R. Weinstein’s simulation and visualization of a still bat [7].

We present JointViewer, a software tool to aid orthopedics researchers in exploring complex, in-vivo joint kinematics. Given bone-geometry data and bone-motion information, JointViewer models and visualizes bone inter-spacing in the joint. Next, it proposes and displays plausible ligament paths which connect bones together. Both types of models are constructed through a distancefield approach. Users can maneuver the bones in a joint for better viewing, see motion relative to a specific bone, or remove bones from a joint. We demonstrate JointViewer’s effectiveness in three applications: examining normal human wrist kinematics, capturing the effect of injury on forearm kinematics, and exploring the kinematic constraints imposed by ligaments in a pigeon shoulder. In all applications, the system effectively highlights subtle yet important relationships among bones and soft-tissue that in previous standard joint visualizations had gone unnoticed.

Researchers in computational condensed matter physics deal with complex data sets consisting of time varying 3D tensor, vector, and scalar quantities. Particularly, in the research of topological defects in nematic liquid crystals (LC) displaying the results of the computer simulation of molecular dynamics presents a challenge. Combining existing immersive and interactive visualization methods we developed new methods that attempt to provide a clear, efficient, and intuitive way to visualize and explore LC data. In addition, the visualization of the data has presented us with a novel method of obtaining the locations of the topological defects present in a liquid crystal system.

Figure 1: Top view of the streamline clusters on the whole brain. Streamlines within the same cluster share the same color. From the picture, the cingulum bundles can be easily identified in two clusters. Neural fibers along the corpus callosum are clustered into coherent bundles. Some of the U-fibers also form clusters.

Multiple sclerosis (MS) is a chronic disease of the central nervous system that predominantly affects young adults during their most productive years. Pathologically, MS is characterized by the presence of areas of demyelination and T-cell predominant perivascular

We present a threads and halos representation for interactive volume rendering of vector-field structure and describe a number of additional components that combine to create effective visualizations of multivalued 3D scientific data. After filtering linear structures, such as flow lines, into a volume representation, we use a multilayer volume rendering approach to simultaneously display this derived volume along with other data values. We demonstrate the utility of threads and halos in clarifying depth relationships within dense renderings and we present results from two scientific applications: visualization of second-order tensor valued magnetic resonance imaging (MRI) data and simulated 3D fluid flow data. In both application areas, the interactivity of the visualizations proved to be important to the domain scientists. Finally, we describe a PC-based implementation of our framework along with domain specific transfer functions, including an exploratory data culling tool, that enable fast data exploration.

No abstract available.

No abstract available.

Must we continue to define a difference between information and scientific visualization? Scientific visualization evolved first in the late 1980’s while information visualization matured in the mid-1990’s. Scientific visualization is frequently considered to focus on the visual display of spatial data associated with scientific processes such as the bonding of molecules in computational chemistry. Information visualization examines developing visual metaphors for non-inherently spatial data such as the exploration of text-based document databases. This panel examines the effective, productive, and perhaps confusing tension between these subfields of visualization by highlighting the following issues:

We present a new method for visualizing 3D volumetric diffusion tensor MR images. We distinguish between linear anisotropy and planar anisotropy and represent values in the two regimes using streamtubes and streamsurfaces, respectively. Streamtubes represent structures with primarily linear diffusion, typically fiber tracts; streamtube direction correlates with tract orientation. The cross-sectional shape and color of each streamtube represent additional information from the diffusion tensor at each point. Streamsurfaces represent structures in which diffusion is primarily planar. Our algorithm chooses a very small representative subset of the streamtubes and streamsurfaces for display. We describe the set of metrics used for the culling process, which reduces visual clutter and improves interactivity. We also generate anatomical landmarks to identify the locations of such structures as the eyes, skull surface, and ventricles. The final models are complex surface geometries that can be imported into many interactive graphics software environments. We describe a virtual environment to interact with these models. Expert feedback from doctors studying changes in white-matter structures after gamma-knife capsulotomy and preoperative planning for brain tumor surgery shows that streamtubes correlate well with major neural structures, the 2D section and geometric landmarks are important in understanding the visualization, and the stereo and interactivity from the virtual environment aid in understanding the complex geometric models.

This paper explores an example SAR data set that has been displayed using a new method of visualising polarimetric data. It is based on an iconic representation of information within a pixel, which allows for the complete polarisation response to be viewed simultaneously across the image. An example from a test site in Sweden is presented, with a detailed description of how such an image can be interpreted. This will be through particular examples of polarimetric responses correlated with features in the landscape.

This paper presents a new method for the visualisation of polarimetric response patterns on a synoptic basis. The aim of this approach is to synthesise traditional polarimetric response graphs with the spatial overview provided by standard remote sensing image processing techniques. By doing this, an effective means of exploring phenomenological polarimetric response patterns is achieved, which may prove to be a valuable input for the development of modelling strategies and the application of polarimetry.

Article Share on CavePainting: a fully immersive 3D artistic medium and interactive experience Authors: Daniel F. Keefe Brown Univ., Providence, RI Brown Univ., Providence, RIView Profile , Daniel Acevedo Feliz Brown Univ., Providence, RI Brown Univ., Providence, RIView Profile , Tomer Moscovich Brown Univ., Providence, RI Brown Univ., Providence, RIView Profile , David H. Laidlaw Brown Univ., Providence, RI Brown Univ., Providence, RIView Profile , Joseph J. LaViola Brown Univ., Providence, RI Brown Univ., Providence, RIView Profile Authors Info & Claims I3D '01: Proceedings of the 2001 symposium on Interactive 3D graphicsMarch 2001Pages 85–93https://doi.org/10.1145/364338.364370Published:01 March 2001Publication History 144citation2,008DownloadsMetricsTotal Citations144Total Downloads2,008Last 12 Months140Last 6 weeks22 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Presents the results of an evaluation of the ARCHAVE (ARCHAeological Virtual Environment) system, an immersive virtual reality (VR) environment for archaeological research. ARCHAVE is implemented in a Cave. The evaluation studied researchers analyzing lamp and coin finds throughout the excavation trenches at the Petra Great Temple site in Jordan. Experienced archaeologists used our system to study excavation data, confirming existing hypotheses and postulating new theories they had not been able to discover without the system. ARCHAVE provided access to the excavation database, and researchers were able to examine the data in the context of a life-size representation of the present-day architectural ruins of the temple. They also had access to a miniature model for site-wide analysis. Because users quickly became comfortable with the interface, they concentrated their efforts on examining the data being retrieved and displayed. The immersive VR visualization of the recovered information gave them the opportunity to explore it in a new and dynamic way and, in several cases, enabled them to make discoveries that opened new lines of investigation about the excavation.

Presents results from a user study that compared six visualization methods for 2D vector data. Two methods used different distributions of short arrows, two used different distributions of integral curves, one used wedges located to suggest flow lines, and the final one was line-integral convolution (LIC). We defined three simple but representative tasks for users to perform using visualizations from each method: (1) locating all critical points in an image, (2) identifying critical point types, and (3) advecting a particle. The results show different strengths and weaknesses for each method. We found that users performed better with methods that: (1) showed the sign of vectors within the vector field, (2) visually represented integral curves, and (3) visually represented the locations of critical points. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand tradeoffs among methods. They may also be useful for evaluating 2D vectors on 2D surfaces embedded in 3D and for defining analogous tasks for 3D visualization methods.

We describe a virtual reality environment for visualizing tensor-valued volumetric datasets acquired with diffusion tensor magnetic resonance imaging (DT-MRI). We have prototyped a virtual environment that displays geometric representations of the volumetric second-order diffusion tensor data and are developing interaction and visualization techniques for two application areas: studying changes in white-matter structures after gamma-knife capsulotomy and pre-operative planning for brain tumor surgery. Our feedback shows that compared to desktop displays, our system helps the user better interpret the large and complex geometric models, and facilitates communication among a group of users.

We present an immersive system for exploring numerically simulated flow data through a model of a coronary artery graft. This tightly-coupled interdisciplinary project is aimed at understanding how to reduce the failure rate of these grafts. The visualization system provides a mechanism for exploring the effect of changes to the geometry, to the flow, and for exploring potential sources of future lesions. The system uses gestural and voice interactions exclusively, moving away from more traditional windows/icons/menus/point-and-click (WIMP) interfaces. We present an example session using the system and discuss our experiences developing, testing, and using it. We describe some of the interaction and rendering techniques that we experimented with and describe their level of success. Our experience suggests that systems like this are exciting to clinical researchers, but conclusive evidence of their value is not yet available.

We present a new visualization method for 2D flows which allows us to combine multiple data values in an image for simultaneous viewing. We utilize concepts from oil painting, art and design as introduced in (Laidlaw et al., 1998) to examine problems within fluid mechanics. We use a combination of discrete and continuous visual elements arranged in multiple layers to visually represent the data. The representations are inspired by the brush strokes artists apply in layers to create an oil painting. We display commonly visualized quantities such as velocity and vorticity together with three additional mathematically derived quantities: the rate of strain tensor, and the turbulent charge and turbulent current. We describe the motivation for simultaneously examining these quantities and use the motivation to guide our choice of visual representation for each particular quantity. We present visualizations of three flow examples and observations concerning some of the physical relationships made apparent by the simultaneous display technique that we employed.

Within biological systems, water molecules undergo continuous stochastic Brownian motion. The diffusion rate can give clues to the structure of the underlying tissues. In some tissues, the rate is anisotropic. Diffusion-rate images can be calculated from diffusion-weighted MRI. A 2D diffusion tensor image (DTI) and an associated anatomical scalar field define seven values at each spatial location. We present two new methods for visually representing DTIs. The first method displays an array of ellipsoids, where the shape of each ellipsoid represents one tensor value. The ellipsoids are all normalized to approximately the same size so that they can be displayed simultaneously in context. The second method uses concepts from oil painting to represent the seven-valued data with multiple layers of varying brush strokes. Both methods successfully display most or all of the information in DTIs and provide exploratory methods for understanding them. The ellipsoid method has a simpler interpretation and explanation than the painting-motivated method; the painting-motivated method displays more of the information and is easier to read quantatively. We demonstrate the methods on images of the mouse spinal cord. The visualizations show significant differences between spinal cords from mice suffering from experimental allergic encephalomyelitis and spinal cords from wild-type mice. The differences are consistent with differences shown histologically and suggest that our new non-invasive imaging methodology and visualization of the results could have early diagnostic value for neurodegenerative diseases.

D.H. Laidlaw replies to comments made on his paper (D.H. Laidlaw et al., ibid., vol. 17, no. 1, p. 74-86, 1998) by H. Soltanian-Zadeh and J.P. Windham (ibid., vol. 17, no. 6, p. 1094, 1998). D.H. Laidlaw says that the concept of optimality always rests on a framework of assumptions. Eigenimage filtering is optimal under a certain set of assumptions. However, D.H. Laidlaw et al.'s voxel histogram method uses different assumptions and, as D.H. Laidlaw demonstrates, produces better results with fewer images. In this communication D.H. Laidlaw first discusses some of the assumptions leading to the eigenimage filtering method, then describe how his assumptions differ and how they lead to a different type of classification method. He then presents results comparing the two methods, addresses the four points raised in the communication by H. Soltanian-Zadeh and J.P. Windham (ibid., vol. 17, no. 6, p. 1094, 1998) and states his conclusions.

We present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because we allow for mixtures of materials and treat voxels as regions, our technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to our approach. First, we assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; we compute the relative proportion of each material in the voxels. Second, we incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, rho(x), from the samples and then looking at the distribution of values that rho(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that we classify is chosen to match the spacing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.

We proposean approach for modeling surface details such as scales, feathers, or thorns. These types of cellular textures require a representation with more detail than texture-mapping but are inconvenient to model with hand-crafted geometry. We generate patterns of geometric elements using a biologically -motivated cellular development simulation together with a constraint to keep the cells on a surface. The surface may be defined by an implicit function, a volume dataset, or a polygonal mesh. Our simulation combines and extends previous work in developmental models and constrained particle systems. Key Words: particle systems, developmental models, data amplification, constraints, texture mapping, bump mapping, displacement mapping 1 Introduction For several years computer graphics researchers and practitioners have been grappling with the problem of creating and displaying surfaces having an organic appearance. Texture maps, bump maps, and related methods often attain the appearanc...

Here, the authors combine a pure phase-encoded magnetic resonance imaging (MRI) method with a new tissue-classification technique to make geometric models of a human tooth. They demonstrate the feasibility of three-dimensional imaging of solids using a conventional 11.7-T NMR spectrometer. In solid-state imaging, confounding line-broadening effects are typically eliminated using coherent averaging methods. Instead, the authors circumvent them by detecting the proton signal at a fixed phase-encode time following the radio-frequency excitation. By a judicious choice of the phase-encode time in the MRI protocol, the authors differentiate enamel and dentine sufficiently to successfully apply a new classification algorithm. This tissue-classification algorithm identifies the distribution of different material types, such as enamel and dentine, in volumetric data. In this algorithm, the authors treat a voxel as a volume, not as a single point, and assume that each voxel may contain more than one material. They use the distribution of MR image intensities within each voxel-sized volume to estimate the relative proportion of each material using a probabilistic approach. This combined approach, involving MRI and data classification, is directly applicable to bone imaging and hard-tissue contrast-based modeling of biological solids.

Constructive Solid Geometry (CSG) is a powerful way of describing solid objects for computer graphics and modeling. The surfaces of any primitive object (such as a cube, sphere or cylinder) can be approximated by polygons. Being abile to find the union, intersection or difference of these objects allows more interesting and complicated polygonal objects to be created. The algorithm presented here performs these set operations on objects constructed from convex polygons. These objects must bound a finite volume, but need not be convex. An object that results from one of these operations also contains only convex polygons, and bounds a finite volume; thus, it can be used in later combinations, allowing the generation of quite complicated objects. Our algorithm is robust and is presented in enough detail to be implemented.