30-Issue 3

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https://diglib.eg.org/handle/10.2312/163

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Prostate Cancer Visualization from MR Imagery and MR Spectroscopy
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Marino, Joseph; Kaufman, Arie; H. Hauser, H. Pfister, and J. J. van Wijk
Prostate cancer is one of the most prevalent cancers among males, and the use of magnetic resonance imaging (MRI) has been suggested for its detection. A framework is presented for scoring and visualizing various MR data in an efficient and intuitive manner. A classification method is introduced where a cumulative score volume is created which takes into account each of three acquisition types. This score volume is integrated into a volume rendering framework which allows the user to view the prostate gland, the multi-modal score values, and the sur- rounding anatomy. A visibility persistence mode is introduced to automatically avoid full occlusion of a selected score and indicate overlaps. The use of GPU-accelerated multi-modal single-pass ray casting provides an inter- active experience. User driven importance rendering allows the user to gain insight into the data and can assist in localization of the disease and treatment planning. We evaluate our results against pathology and radiologists' determinations.
Visual Coherence for Large-Scale Line-Plot Visualizations
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Muigg, Philipp; Hadwiger, Markus; Doleisch, Helmut; Gröller, Eduard; H. Hauser, H. Pfister, and J. J. van Wijk
Displaying a large number of lines within a limited amount of screen space is a task that is common to many different classes of visualization techniques such as time-series visualizations, parallel coordinates, link-node diagrams, and phase-space diagrams. This paper addresses the challenging problems of cluttering and overdraw inherent to such visualizations. We generate a 2x2 tensor field during line rasterization that encodes the distribution of line orientations through each image pixel. Anisotropic diffusion of a noise texture is then used to generate a dense, coherent visualization of line orientation. In order to represent features of different scales, we employ a multi-resolution representation of the tensor field. The resulting technique can easily be applied to a wide variety of line-based visualizations. We demonstrate this for parallel coordinates, a time-series visualization, and a phase-space diagram. Furthermore, we demonstrate how to integrate a focus+context approach by incorporating a second tensor field. Our approach achieves interactive rendering performance for large data sets containing millions of data items, due to its image-based nature and ease of implementation on GPUs. Simulation results from computational fluid dynamics are used to evaluate the performance and usefulness of the proposed method.
PaperVis: Literature Review Made Easy
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Chou, Jia -Kai; Yang, C. -K.; H. Hauser, H. Pfister, and J. J. van Wijk
Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.
Topology-based Visualization of Transformation Pathways in Complex Chemical Systems
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Beketayev, Kenes; Weber, G. H.; Haranczyk, M.; Bremer, P.-T.; Hlawitschka, M.; Hamann, B.; H. Hauser, H. Pfister, and J. J. van Wijk
Studying transformation in a chemical system by considering its energy as a function of coordinates of the system's components provides insight and changes our understanding of this process. Currently, a lack of effective visualization techniques for high-dimensional energy functions limits chemists to plot energy with respect to one or two coordinates at a time. In some complex systems, developing a comprehensive understanding requires new visualization techniques that show relationships between all coordinates at the same time. We propose a new visualization technique that combines concepts from topological analysis, multi-dimensional scaling, and graph layout to enable the analysis of energy functions for a wide range of molecular structures. We demonstrate our technique by studying the energy function of a dimer of formic and acetic acids and a LTA zeolite structure, in which we consider diffusion of methane.
Visualization of Time-Series Data in Parameter Space for Understanding Facial Dynamics
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Tam, Gary K. L.; Fang, H.; Aubrey, A. J.; Grant, P. W.; Rosin, P. L.; Marshall, D.; Chen, M.; H. Hauser, H. Pfister, and J. J. van Wijk
Over the past decade, computer scientists and psychologists have made great efforts to collect and analyze facial dynamics data that exhibit different expressions and emotions. Such data is commonly captured as videos and are transformed into feature-based time-series prior to any analysis. However, the analytical tasks, such as expression classification, have been hindered by the lack of understanding of the complex data space and the associated algorithm space. Conventional graph-based time-series visualization is also found inadequate to support such tasks. In this work, we adopt a visual analytics approach by visualizing the correlation between the algorithm space and our goal classifying facial dynamics. We transform multiple feature-based time-series for each expression in measurement space to a multi-dimensional representation in parameter space. This enables us to utilize parallel coordinates visualization to gain an understanding of the algorithm space, providing a fast and cost-effective means to support the design of analytical algorithms.
Pathway Preserving Representation of Metabolic Networks
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Lambert, Antoine; Dubois, J.; Bourqui, Romain; H. Hauser, H. Pfister, and J. J. van Wijk
Improvements in biological data acquisition and genomes sequencing now allow to reconstruct entire metabolic networks of many living organisms. The size and complexity of these networks prohibit manual drawing and thereby urge the need of dedicated visualization techniques. An efficient representation of such a network should preserve the topological information of metabolic pathways while respecting biological drawing conventions. These constraints complicate the automatic generation of such visualization as it raises graph drawing issues. In this paper we propose a method to lay out the entire metabolic network while preserving the pathway information as much as possible. That method is flexible as it enables the user to define whether or not node duplication should be performed, to preserve or not the network topology. Our technique combines partitioning, node placement and edge bundling to provide a pseudo-orthogonal visualization of the metabolic network. To ease pathway information retrieval, we also provide complementary interaction tools that emphasize relevant pathways in the entire metabolic context.
Flowstrates: An Approach for Visual Exploration of Temporal Origin-Destination Data
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Boyandin, Ilya; Bertini, Enrico; Bak, Peter; Lalanne, Denis; H. Hauser, H. Pfister, and J. J. van Wijk
Many origin-destination datasets have become available in the recent years, e.g. flows of people, animals, money, material, or network traffic between pairs of locations, but appropriate techniques for their exploration still have to be developed. Especially, supporting the analysis of datasets with a temporal dimension remains a significant challenge. Many techniques for the exploration of spatio-temporal data have been developed, but they prove to be only of limited use when applied to temporal origin-destination datasets.We present Flowstrates, a new interactive visualization approach in which the origins and the destinations of the flows are displayed in two separate maps, and the changes over time of the flow magnitudes are represented in a separate heatmap view in the middle. This allows the users to perform spatial visual queries, focusing on different regions of interest for the origins and destinations, and to analyze the changes over time provided with the means of flow ordering, filtering and aggregation in the heatmap. In this paper, we discuss the challenges associated with the visualization of temporal origin-destination data, introduce our solution, and present several usage scenarios showing how the tool we have developed supports them.
A User Study of Visualization Effectiveness Using EEG and Cognitive Load
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Anderson, Erik W.; Potter, K. C.; Matzen, L. E.; Shepherd, J. F.; Preston, G. A.; Silva, C. T.; H. Hauser, H. Pfister, and J. J. van Wijk
Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.
Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Berger, Wolfgang; Piringer, H.; Filzmoser, P.; Gröller, Eduard; H. Hauser, H. Pfister, and J. J. van Wijk
Systems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play an important role in science and engineering. If evaluating the system is expensive, however, an analysis is often limited to a small number of sample points. The main contribution of this paper is an interactive approach to enable a continuous analysis of a sampled parameter space with respect to multiple target values. We employ methods from statistical learning to predict results in real-time at any user-defined point and its neighborhood. In particular, we describe techniques to guide the user to potentially interesting parameter regions, and we visualize the inherent uncertainty of predictions in 2D scatterplots and parallel coordinates. An evaluation describes a realworld scenario in the application context of car engine design and reports feedback of domain experts. The results indicate that our approach is suitable to accelerate a local sensitivity analysis of multiple target dimensions, and to determine a sufficient local sampling density for interesting parameter regions.
A Shader Framework for Rapid Prototyping of GPU-Based Volume Rendering
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Rieder, Christian; Palmer, Stephan; Link, Florian; Hahn, Horst K.; H. Hauser, H. Pfister, and J. J. van Wijk
In this paper, we present a rapid prototyping framework for GPU-based volume rendering. Therefore, we propose a dynamic shader pipeline based on the SuperShader concept and illustrate the design decisions. Also, important requirements for the development of our system are presented. In our approach, we break down the rendering shader into areas containing code for different computations, which are defined as freely combinable, modularized shader blocks. Hence, high-level changes of the rendering configuration result in the implicit modification of the underlying shader pipeline. Furthermore, the prototyping system allows inserting custom shader code between shader blocks of the pipeline at run-time. A suitable user interface is available within the prototyping environment to allow intuitive modification of the shader pipeline. Thus, appropriate solutions for visualization problems can be interactively developed. We demonstrate the usage and the usefulness of our framework with implementations of dynamic rendering effects for medical applications.
An Evaluation of Visualization Techniques to Illustrate Statistical Deformation Models
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Caban, Jesus J.; Rheingans, Penny; Yoo, T.; H. Hauser, H. Pfister, and J. J. van Wijk
As collections of 2D/3D images continue to grow, interest in effective ways to visualize and explore the statistical morphological properties of a group of images has surged. Recently, deformation models have emerged as simple methods to capture the variability and statistical properties of a collection of images. Such models have proven to be effective in tasks such as image classification, generation, registration, segmentation, and analysis of modes of variation. A crucial element missing from most statistical models has been an effective way to summarize and visualize the statistical morphological properties of a group of images. This paper evaluates different visualization techniques that can be extended and used to illustrate the information captured by such statistical models. First, four illustration techniques are described as methods to summarize the statistical morphological properties as captured by deformation models. Second, results of a user study conducted to compare the effectiveness of each visualization technique are presented. After comparing the performance of 40 subjects, we found that statistical annotation techniques present significant benefits when analyzing the structural properties of a group of images.
A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Hao, M. C.; Janetzko, H.; Mittelstädt, S.; Hill, W.; Dayal, U.; Keim, D. A.; Marwah, M.; Sharma, R. K.; H. Hauser, H. Pfister, and J. J. van Wijk
Time series prediction methods are used on a daily basis by analysts for making important decisions. Most of these methods use some variant of moving averages to reduce the number of data points before prediction. However, to reach a good prediction in certain applications (e.g., power consumption time series in data centers) it is important to preserve peaks and their patterns. In this paper, we introduce automated peak-preserving smoothing and prediction algorithms, enabling a reliable long term prediction for seasonal data, and combine them with an advanced visual interface: (1) using high resolution cell-based time series to explore seasonal patterns, (2) adding new visual interaction techniques (multi-scaling, slider, and brushing & linking) to incorporate human expert knowledge, and (3) providing both new visual accuracy color indicators for validating the predicted results and certainty bands communicating the uncertainty of the prediction. We have integrated these techniques into a wellfitted solution to support the prediction process, and applied and evaluated the approach to predict both power consumption and server utilization in data centers with 70-80% accuracy.
Energy-scale Aware Feature Extraction for Flow Visualization
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Pobitzer, A.; Tutkun, M.; Andreassen, Ø.; Fuchs, R.; Peikert, R.; Hauser, H.; H. Hauser, H. Pfister, and J. J. van Wijk
In the visualization of flow simulation data, feature detectors often tend to result in overly rich response, making some sort of filtering or simplification necessary to convey meaningful images. In this paper we present an approach that builds upon a decomposition of the flow field according to dynamical importance of different scales of motion energy. Focusing on the high-energy scales leads to a reduction of the flow field while retaining the underlying physical process. The presented method acknowledges the intrinsic structures of the flow according to its energy and therefore allows to focus on the energetically most interesting aspects of the flow. Our analysis shows that this approach can be used for methods based on both local feature extraction and particle integration and we provide a discussion of the error caused by the approximation. Finally, we illustrate the use of the proposed approach for both a local and a global feature detector and in the context of numerical flow simulations.
Progressive Splatting of Continuous Scatterplots and Parallel Coordinates
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Heinrich, Julian; Bachthaler, S.; Weiskopf, Daniel; H. Hauser, H. Pfister, and J. J. van Wijk
Continuous scatterplots and parallel coordinates are used to visualize multivariate data defined on a continuous domain. With the existing techniques, rendering such plots becomes prohibitively slow, especially for large scientific datasets. This paper presents a scalable and progressive rendering algorithm for continuous data plots that allows exploratory analysis of large datasets at interactive framerates. The algorithm employs splatting to produce a series of plots that are combined using alpha blending to achieve a progressively improving image. For each individual frame, splats are obtained by transforming Gaussian density kernels from the 3-D domain of the input dataset to the respective data domain. A closed-form analytic description of the resulting splat footprints is derived to allow pre-computation of splat textures for efficient GPU rendering. The plotting method is versatile because it supports arbitrary reconstruction or interpolation schemes for the input data and the splatting technique is scalable because it chooses splat samples independently from the size of the input dataset. Finally, the effectiveness of the method is compared to existing techniques regarding rendering performance and quality.
Anatomy-Guided Multi-Level Exploration of Blood Flow in Cerebral Aneurysms
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Neugebauer, Mathias; Janiga, Gabor; Beuing, Oliver; Skalej, Martin; Preim, Bernhard; H. Hauser, H. Pfister, and J. J. van Wijk
For cerebral aneurysms, the ostium, the area of inflow, is an important anatomic landmark, since it separates the pathological vessel deformation from the healthy parent vessel. A better understanding of the inflow characteristics, the flow inside the aneurysm and the overall change of pre- and post-aneurysm flow in the parent vessel provide insights for medical research and the development of new risk-reduced treatment options. We present an approach for a qualitative, visual flow exploration that incorporates the ostium and derived anatomical landmarks. It is divided into three scopes: a global scope for exploration of the in- and outflow, an ostium scope that provides characteristics of the flow profile close to the ostium and a local scope for a detailed exploration of the flow in the parent vessel and the aneurysm. The approach was applied to five representative datasets, including measured and simulated blood flow. Informal interviews with two board-certified radiologists confirmed the usefulness of the provided exploration tools and delivered input for the integration of the ostium-based flow analysis into the overall exploration workflow.
Comparison of Multiple Weighted Hierarchies: Visual Analytics for Microbe Community Profiling
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Dinkla, Kasper; Westenberg, M. A.; Timmerman, H. M.; Hijum, S.A.F.T. van; Wijk, J. J. van; H. Hauser, H. Pfister, and J. J. van Wijk
We propose visual analytics techniques to support concurrent comparison of hundreds of cumulatively weighted instances of a single hierarchy. This includes a node-link representation of the hierarchy where nodes depict the weights of all instances with high-density heat maps that are grouped and aligned to ease cross-referencing. Hierarchy exploration is facilitated by smoothly animated expansion and collapse of its branches. Detailed infor- mation about hierarchy structure, weights, and meta-data is provided by secondary linked visualizations. These techniques have been implemented in a prototype tool, in which the computational analysis concerns have been strictly separated from the visualization concerns. The analysis algorithms are extensible via a script engine. We discuss the effectiveness of our techniques for the visual analytic process of microbe community profiling experts.
A Framework for Exploring Multidimensional Data with 3D Projections
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Poco, Jorge; Etemadpour, Ronak; Paulovich, F. V.; Long, T. V.; Rosenthal, P.; Oliveira, M. C. F.; Linsen, Lars; Minghim, R.; H. Hauser, H. Pfister, and J. J. van Wijk
Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e.g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least- Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework's applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.
Dynamic Insets for Context-Aware Graph Navigation
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Ghani, Sohaib; Riche, N. Henry; Elmqvist, Niklas; H. Hauser, H. Pfister, and J. J. van Wijk
Maintaining both overview and detail while navigating in graphs, such as road networks, airline route maps, or social networks, is difficult, especially when targets of interest are located far apart. We present a navigation technique called Dynamic Insets that provides context awareness for graph navigation. Dynamic insets utilize the topological structure of the network to draw a visual inset for off-screen nodes that shows a portion of the surrounding area for links leaving the edge of the screen. We implement dynamic insets for general graph navigation as well as geographical maps. We also present results from a set of user studies that show that our technique is more efficient than most of the existing techniques for graph navigation in different networks.
WaveMap: Interactively Discovering Features From Protein Flexibility Matrices Using Wavelet-based Visual Analytics
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Barlowe, Scott; Liu, Yujie; Yang, Jing; Livesay, Dennis R.; Jacobs, Donald J.; Mottonen, James; Verma, Deeptak; H. Hauser, H. Pfister, and J. J. van Wijk
The knowledge gained from biology datasets can streamline and speed-up pharmaceutical development. However, computational models generate so much information regarding protein behavior that large-scale analysis by traditional methods is almost impossible. The volume of data produced makes the transition from data to knowledge difficult and hinders biomedical advances. In this work, we present a novel visual analytics approach named WaveMap for exploring data generated by a protein flexibility model. WaveMap integrates wavelet analysis, visualizations, and interactions to facilitate the browsing, feature identification, and comparison of protein attributes represented by two-dimensional plots. We have implemented a fully working prototype of WaveMap and illustrate its usefulness through expert evaluation and an example scenario.
Interactive Visual Analysis of Temporal Cluster Structures
(The Eurographics Association and Blackwell Publishing Ltd., 2011) Turkay, Cagatay; Parulek, J.; Reuter, N.; Hauser, Helwig; H. Hauser, H. Pfister, and J. J. van Wijk
Cluster analysis is a useful method which reveals underlying structures and relations of items after grouping them into clusters. In the case of temporal data, clusters are defined over time intervals where they usually exhibit structural changes. Conventional cluster analysis does not provide sufficient methods to analyze these structural changes, which are, however, crucial in the interpretation and evaluation of temporal clusters. In this paper, we present two novel and interactive visualization techniques that enable users to explore and interpret the structural changes of temporal clusters. We introduce the temporal cluster view, which visualizes the structural quality of a number of temporal clusters, and temporal signatures, which represents the structure of clusters over time. We discuss how these views are utilized to understand the temporal evolution of clusters. We evaluate the proposed techniques in the cluster analysis of mixed lipid bilayers.

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