27-Issue 3

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https://diglib.eg.org/handle/10.2312/136

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Automatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi-fields
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Jänicke, Heike; Böttinger, Michael; Tricoche, Xavier; Scheuermann, Gerik; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Current unsteady multi-field simulation data-sets consist of millions of data-points. To efficiently reduce this enormous amount of information, local statistical complexity was recently introduced as a method that identifies distinctive structures using concepts from information theory. Due to high computational costs this method was so far limited to 2D data. In this paper we propose a new strategy for the computation that is substantially faster and allows for a more precise analysis. The bottleneck of the original method is the division of spatio-temporal configurations in the field (light-cones) into different classes of behavior. The new algorithm uses a density-driven Voronoi tessellation for this task that more accurately captures the distribution of configurations in the sparsely sampled high-dimensional space. The efficient computation is achieved using structures and algorithms from graph theory. The ability of the method to detect distinctive regions in 3D is illustrated using flow and weather simulations.
Abstractive Representation and Exploration of Hierarchically Clustered Diffusion Tensor Fiber Tracts
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Chen, Wei; Zhang, Song; Correia, Stephen; Ebert, David S.; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Diffusion tensor imaging (DTI) has been used to generate fibrous structures in both brain white matter and muscles. Fiber clustering groups the DTI fibers into spatially and anatomically related tracts. As an increasing number of fiber clustering methods have been recently developed, it is important to display, compare, and explore the clustering results efficiently and effectively. In this paper, we present an anatomical visualization technique that reduces the geometric complexity of the fiber tracts and emphasizes the high-level structures. Beginning with a volumetric diffusion tensor image, we first construct a hierarchical clustering representation of the fiber bundles. These bundles are then reformulated into a 3D multi-valued volume data. We then build a set of geometric hulls and principal fibers to approximate the shape and orientation of each fiber bundle. By simultaneously visualizing the geometric hulls, individual fibers, and other data sets such as fractional anisotropy, the overall shape of the fiber tracts are highlighted, while preserving the fibrous details. A rater with expert knowledge of white matter structure has evaluated the resulting interactive illustration and confirmed the improvement over straightforward DTI fiber tract visualization.
A Screen Space Quality Method for Data Abstraction
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Johansson, Jimmy; Cooper, Matthew; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
The rendering of large data sets can result in cluttered displays and non-interactive update rates, leading to time consuming analyses. A straightforward solution is to reduce the number of items, thereby producing an abstraction of the data set. For the visual analysis to remain accurate, the graphical representation of the abstraction must preserve the significant features present in the original data. This paper presents a screen space quality method, based on distance transforms, that measures the visual quality of a data abstraction. This screen space measure is shown to better capture significant visual structures in data, compared with data space measures. The presented method is implemented on the GPU, allowing interactive creation of high quality graphical representations of multivariate data sets containing tens of thousands of items
Interactive Visualization of Multimodal Volume Data for Neurosurgical Tumor Treatment
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Rieder, Christian; Ritter, Felix; Raspe, Matthias; Peitgen, Heinz-Otto; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
We present novel interactive methods for the visualization of multimodal volume data as used in neurosurgical therapy planning. These methods allow surgeons to explore multimodal volumes and focus on functional data and lesions. Computer graphics techniques are proposed to create expressive visualizations at interactive frame rates to reduce time-consuming and complex interaction with the medical data. Contributions of our work are the distance-based enhancements of functional data and lesions which allows the surgeon to perceive functional and anatomical structures at once and relate them directly to the intervention. In addition we propose methods for the visual exploration of the path to the structures of interest, to enhance anatomical landmarks, and to provide additional depth indicators. These techniques have been integrated in a visualization prototype that provides interaction capabilities for finding the optimal therapeutic strategy for the neurosurgeon.
Visual Inspection of Multivariate Graphs
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Pretorius, A. Johannes; Wijk, Jarke J. van; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, "which edge types are activated by specific node attributes?" and, "how and from where can I reach specific types of nodes?" To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real-world systems.
Visualizing Genome Expression and Regulatory Network Dynamics in Genomic and Metabolic Context
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Westenberg, Michel A.; Hijum, S. A. F. T. van; Kuipers, O. P.; Roerdink, Jos B. T. M.; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
DNA microarrays are used to measure the expression levels of thousands of genes simultaneously. In a time series experiment, the gene expressions are measured as a function of time. We present an application for integrated visualization of genome expression and network dynamics in both regulatory networks and metabolic pathways. Integration of these two levels of cellular processes is necessary, since it provides the link between the measure- ments at the transcriptional level (gene expression levels approximated from microarray data) and the phenotype (the observable characteristics of an organism) at the functional and behavioral level. The integration requires visualization approaches besides traditional clustering and statistical analysis methods. Our application can (i) visualize the data from time series experiments in the context of a regulatory network and KEGG metabolic pathways; (ii) identify and visualize active regulatory subnetworks from the gene expression data; (iii) perform a statistical test to identify and subsequently visualize pathways that are affected by differentially expressed genes. We present a case study, which demonstrates that our approach and application both facilitates and speeds up data analysis tremendously in comparison to a more traditional approach that involves many manual, laborious, and error-prone steps.
Physically-based Dye Advection for Flow Visualization
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Li, Guo-Shi; Tricoche, Xavier; Hansen, Charles; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Dye advection is widely used in experimental flow analysis but has seen less use for visualization in computational fluid dynamics. One possible reason for this disconnect is the inaccuracy of the texture-based approach, which is prone to artifacts caused by numeric diffusion and mass fluctuation. In this paper, we introduce a novel 2D dye advection scheme for flow visualization based on the concept of control volume analysis typically used in computational fluid dynamics. The evolution of dye patterns in the flow field is achieved by advecting individual control volumes, which collectively cover the entire spatial domain. The local variation of dye material, represented as a piecewise quasi-parabolic function, is integrated within each control volume resulting in mass conserving transport without excessive numerical diffusion. Due to its physically based formulation, this approach is capable of conveying intricate flow structures not shown in the traditional dye advection schemes while avoiding visual artifacts.
Computing Local Signed Distance Fields for Large Polygonal Models
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Chang, Byungjoon; Cha, Deukhyun; Ihm, Insung; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
The signed distance field for a polygonal model is a useful representation that facilitates efficient computation in many visualization and geometric processing tasks. Often it is more effective to build a local distance field only within a narrow band around the surface that holds local geometric information for the model. In this paper, we present a novel technique to construct a volumetric local signed distance field of a polygonal model. To compute the local field efficiently, exactly those cells that cross the polygonal surface are found first through a new voxelization method, building a list of intersecting triangles for each boundary cell. After their neighboring cells are classified, the triangle lists are exploited to compute the local signed distance field with minimized voxel-totriangle distance computations. While several efficient methods for computing the distance field, particularly those harnessing the graphics processing unit's (GPU's) processing power, have recently been proposed, we focus on a CPU-based technique, intended to deal flexibly with large polygonal models and high-resolution grids that are often too bulky for GPU computation.
Quality Isosurface Mesh Generation Using an Extended Marching Cubes Lookup Table
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Raman, Sundaresan; Wenger, Rephael; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Animating Causal Overlays
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Bartram, Lyn; Yao, Miao; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Most approaches to representing causality, such as the common causal graph, require a separate and static view, but in many cases it is useful to add the dimension of causality to the context of an existing visualization. Building on research from perceptual psychology that shows the perception of causality is a low-level visual event derived from certain types of motion, we are investigating how to add animated causal representations, called visual causal vectors, onto other visualizations. We refer to these as causal overlays. Our initial experimental results show this approach has great potential but that extra cues are needed to elicit the perception of causality when the motions are overlaid on other graphical objects. In this paper we describe the approach and report on a study that examined two issues of this technique: how to accurately convey the causal flow and how to represent the strength of the causal effect.
Concurrent Viewing of Multiple Attribute-Specific Subspaces
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Sisneros, Robert; Johnson, C. Ryan; Huang, Jian; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
In this work we present a point classification algorithm for multi-variate data. Our method is based on the concept of attribute subspaces, which are derived from a set of user specified attribute target values. Our classification approach enables users to visually distinguish regions of saliency through concurrent viewing of these subspaces in single images. We also allow a user to threshold the data according to a specified distance from attribute target values. Based on the degree of thresholding, the remaining data points are assigned radii of influence that are used for the final coloring. This limits the view to only those points that are most relevant, while maintaining a similar visual context.
Interaction-Dependent Semantics for Illustrative Volume Rendering
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Rautek, Peter; Bruckner, Stefan; Gröller, M. Eduard; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
In traditional illustration the choice of appropriate styles and rendering techniques is guided by the intention of the artist. For illustrative volume visualizations it is difficult to specify the mapping between the 3D data and the visual representation that preserves the intention of the user. The semantic layers concept establishes this mapping with a linguistic formulation of rules that directly map data features to rendering styles. With semantic layers fuzzy logic is used to evaluate the user defined illustration rules in a preprocessing step. In this paper we introduce interaction-dependent rules that are evaluated for each frame and are therefore computationally more expensive. Enabling interaction-dependent rules, however, allows the use of a new class of semantics, resulting in more expressive interactive illustrations. We show that the evaluation of the fuzzy logic can be done on the graphics hardware enabling the efficient use of interaction-dependent semantics. Further we introduce the flat rendering mode and discuss how different rendering parameters are influenced by the rule base. Our approach provides high quality illustrative volume renderings at interactive frame rates, guided by the specification of illustration rules.
FromWeb Data to Visualization via Ontology Mapping
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Gilson, Owen; Silva, Nuno; Grant, Phil W.; Chen, Min; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
In this paper, we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages.
Extraction Of Feature Lines On Surface Meshes Based On Discrete Morse Theory
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Sahner, Jan; Weber, Britta; Prohaska, Steffen; Lamecker, Hans; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
We present an approach for extracting extremal feature lines of scalar indicators on surface meshes, based on discrete Morse Theory. By computing initial Morse-Smale complexes of the scalar indicators of the mesh, we obtain a candidate set of extremal feature lines of the surface. A hierarchy of Morse-Smale complexes is computed by prioritizing feature lines according to a novel criterion and applying a cancellation procedure that allows us to select the most significant lines. Given the scalar indicators on the vertices of the mesh, the presented feature line extraction scheme is interpolation free and needs no derivative estimates. The technique is insensitive to noise and depends only on one parameter: the feature significance. We use the technique to extract surface features yielding impressive, non photorealistic images.
Lagrangian Visualization of Flow-Embedded Surface Structures
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Garth, Christoph; Wiebel, Alexander; Tricoche, Xavier; Joy, Ken; Scheuermann, Gerik; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
The notions of Finite-Time Lyapunov Exponent (FTLE) and Lagrangian Coherent Structures provide a strong framework for the analysis and visualization of complex technical flows. Their definition is simple and intuitive, and they are built on a deep theoretical foundation. We apply these concepts to enable the analysis of flows in the immediate vicinity of the boundaries of flow-embedded objects by limiting the Lagrangian analysis to surfaces closely neighboring these boundaries. To this purpose, we present an approach to approximate FTLE fields over such surfaces. Furthermore, we achieve an effective depiction of boundary-related flow structures such as separation and attachment over object boundaries and specific insight into the surrounding flow using several specifically chosen visualization techniques. We document the applicability of our methods by presenting a number of application examples.
Illustrative Parallel Coordinates
(The Eurographics Association and Blackwell Publishing Ltd., 2008) McDonnell, Kevin T.; Mueller, Klaus; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge-bundling through splines; (b) visualization of "branched" clusters to reveal the distribution of the data; (c) opacity-based hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes; and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non-experts.
Navigation and Exploration of Interconnected Pathways
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Streit, Marc; Kalkusch, M.; Kashofer, K.; Schmalstieg, Dieter; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Visualizing pathways, i. e. models of cellular functional networks, is a challenging task in computer assisted biomedicine. Pathways are represented as large collections of interwoven graphs, with complex structures present in both the individual graphs and their interconnections. This situation requires the development of novel visualization techniques to allow efficient visual exploration. We present the Caleydo framework, which incorporates a number of approaches to handle such pathways. Navigation in the network of pathways is facilitated by a hierarchical approach which dynamically selects a working set of individual pathways for closer inspection. These pathways are interactively rendered together with visual interconnections in a 2.5D view using graphics hardware acceleration. The layout of individual graphs is not computed automatically, but taken from the KEGG and BioCarta databases, which use layouts that life scientists are familiar with. Therefore they encode essential meta-information. While the KEGG and BioCarta pathways use a pre-defined layout, interactions such as linking+ brushing, neighborhood search or detail on demand are still fully interactive in Caleydo. We have evaluated Caleydo with pathologists working on the determination of unknown gene functions. Informal experiences confirm that Caleydo is useful in both generating and validating such hypotheses. Even though the presented techniques are applied to medical pathways, the proposed way of interaction is not limited to cellular processes and therefore has the potential to open new possibilities in other fields of application.
Classification and Uncertainty Visualization of Dendritic Spines from Optical Microscopy Imaging
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Janoos, Firdaus; Nouansengsy, Boonthanome; Xu, Xiaoyin; Machiraju, Raghu; Wong, Stephen T. C.; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Abstract Neuronal dendrites and their spines affect the connectivity of neural networks, and play a significant role in many neurological conditions. Neuronal function is observed to be closely correlated with the appearance, disappearance and morphology of the spines. Automatic 3-D reconstruction of neurons from light microscopy images, followed by the identification, classification and visualization of dendritic spines is therefore essential for studying neuronal physiology and biophysical properties. In this paper, we present a method to reconstruct dendrites using a surface representation of the dendrite. The 1-D skeleton of the dendritic surface is then extracted by a medial geodesic function that is robust and topologically correct. This is followed by a Bayesian identification and classification of the spines. The dendrite and spines are visualized in a manner that displays the spines' types and the inherent uncertainty in identification and classification. We also describe a user study conducted to validate the accuracy of the classification and the efficacy of the visualization.
Towards Closing the Analysis Gap: Visual Generation of Decision Supporting Schemes from Raw Data
(The Eurographics Association and Blackwell Publishing Ltd., 2008) May, Thorsten; Kohlhammer, Jörn; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
The derivation, manipulation and verification of analytical models from raw data is a process which requires a transformation of information across different levels of abstraction. We introduce a concept for the coupling of data classification and interactive visualization in order to make this transformation visible and steerable for the human user. Data classification techniques generate mappings that formally group data items into categories. Interactive visualization includes the user into an iterative refinement process. The user identifies and selects interesting patterns to define these categories. The following step is the transformation of a visible pattern into the formal definition of a classifier. In the last step the classifier is transformed back into a pattern that is blended with the original data in the same visual display. Our approach allows in intuitive assessment of a formal classifier and its model, the detection of outliers and the handling of noisy data using visual pattern-matching. We instantiated the concept using decision trees for classification and KVMaps as the visualization technique. The generation of a classifier from visual patterns and its verification is transformed from a cognitive to a mostly pre-cognitive task.
Visual Clustering in Parallel Coordinates
(The Eurographics Association and Blackwell Publishing Ltd., 2008) Zhou, Hong; Yuan, Xiaoru; Qu, Huamin; Cui, Weiwei; Chen, Baoquan; A. Vilanova, A. Telea, G. Scheuermann, and T. Moeller
Parallel coordinates have been widely applied to visualize high-dimensional and multivariate data, discerning patterns within the data through visual clustering. However, the effectiveness of this technique on large data is reduced by edge clutter. In this paper, we present a novel framework to reduce edge clutter, consequently improving the effectiveness of visual clustering. We exploit curved edges and optimize the arrangement of these curved edges by minimizing their curvature and maximizing the parallelism of adjacent edges. The overall visual clustering is improved by adjusting the shape of the edges while keeping their relative order. The experiments on several representative datasets demonstrate the effectiveness of our approach.

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