Dynamic Data Visualization Research Articles

Visualization of high dynamic range data in geosciences

High dynamic range data, the magnitudes of which span a wide range of scales, are pervasive in the geosciences. Simulations or measurements of many important geophysical processes, such as earthquakes, mantle temperature fluctuations, generate various high dynamic range data sets, in the form of volume or surface. Effective visualization of such datasets is vital for understanding such complex geophysical phenomena and poses a new challenge for both geoscientists and visualization scientists. We describe the general aspects of high dynamic range datasets in the geophysical sciences and provide three case studies of visualizing such data using the techniques we have developed recently. They include: (1) visualizing the Earth’s mantle structures using high dynamic volume volume visualization (HDR VolVis); (2) visualizing surface ruptures during earth quakes using an interference-based method; (3) visualizing tsunami waves again using interference-based method. To improve the performance of visualizing these datasets, which are usually large in size, modern commodity graphics hardware are leveraged to provide simultaneously efficient simulation and visualization.

An adaptive framework for visualizing unstructured grids with time-varying scalar fields

Interactive visualization of time-varying volume data is essential for many scientific simulations. This is a challenging problem since this data is often large, can be organized in different formats (regular or irregular grids), with variable instances of time (from hundreds to thousands) and variable domain fields. It is common to consider subsets of this problem, such as time-varying scalar fields (TVSFs) on static structured grids, which are suitable for compression using multi-resolution techniques and can be efficiently rendered using texture-mapping hardware. In this work we propose a rendering system that considers unstructured grids, which do not have the same regular properties crucial to compression and rendering. Our solution simultaneously leverages multiple processors and the graphics processing unit (GPU) to perform decompression, level-of-detail selection, and volume visualization of dynamic data. The resulting framework is general enough to adaptively handle visualization tasks such as direct volume rendering and isosurfacing while allowing the user to control the speed and quality of the animation.

Magnetic Resonance Imaging Workbench: Analysis and Visualization of Dynamic Contrast-enhanced MR Imaging Data

Magnetic Resonance Imaging Workbench (MRIW) allows analysis of T1- and T2*-weighted dynamic contrast-enhanced magnetic resonance imaging data sets to extract tissue permeability and perfusion characteristics by using standard pharmacokinetic models. Parametric maps are calculated from individual pixel enhancement curves in regions of interest (ROIs) and displayed as color overlays on the anatomic images. User-defined ROIs can be saved to ensure consistency of later reanalysis. Individual parametric maps are visualized together with user-selected parameter time-series plots. The following selections are available: overall ROI enhancement curve and fit, histogram, and individual pixel enhancement curve and fit. Summary data (transfer constant, leakage space, rate constant, integrated area under the gadolinium curve after 60 seconds, relative blood volume, relative blood flow, and mean transit time) may be exported to permanent storage along with per-pixel results for statistical analysis. Numerical values for parameters are displayed below the plot for easy reference. The dynamic range of plots and parametric map overlays is interactively adjustable. Viewing individual enhancement curves and parametric maps allows radiologists to investigate the heterogeneity of contrast agent kinetics for lesion characterization and to scrutinize serial changes in response to therapy. MRIW is written in IDL, enabling it to be used on a variety of computer systems.

Using video to investigate preschool classroom interaction: education research assumptions and methodological practices

This article reports on the use of video to collect dynamic visual data in education research and proposes that using visual technologies to collect data can give new insights into classroom interaction. Video data unveil how young children use the full range of material and bodily resources available to them to make and express meaning, forcing a reconsideration of Vygotskian accounts of the relationship between thought and language by producing grounded evidence for a pluralistic interpretation of the construction and negotiation of meaning. In addition to challenging language-biased approaches to classroom interaction, using video to collect data also forces a reexamination of established methodological practices. Drawing on data from ESRC-funded ethnographic video case studies of 3-year-old children communicating at home and in a preschool playgroup, this article discusses methodological and ethical dilemmas encountered in the collection and transcription, or representation, of dynamic visual data, arguing that visual data gives insights into aspects of communicative behaviour previously unaccounted for in early years education research.

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.

Dynamic visual data mining: biological sequence analysis and annotation using SeqVISTA

In the post-genomic era, the volume of public sequence databases is increasing exponentially and visualisation-centric techniques have become more and more important in biological sequence analysis and annotation. In this paper, we present a methodology called dynamic visual data mining (DVDM), which combines biological object modelling, interactive display, and data analysis tools into one integrative platform. Using Java Development Kit v1.4, an object-oriented software named SeqVISTA has been developed based on DVDM. To illustrate the application of SeqVISTA, the following examples are shown: regular expression pattern matching; comparative analysis of alternative exon splicing patterns; Fourier analyses; exon prediction (MZEF and GENSCAN). Overall, we argue that DVDM is an important technique for biologists to unveil the information hidden behind the large genomic and proteomic databases, and SeqVISTA provides a versatile tool that integrates multiple computational algorithms for meeting biologists' data mining needs.

Compound Brushing Explained

This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach, brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure and control various common types of brushing techniques currently used in dynamic data visualization but also to investigate new brushing techniques.

A post-processing tool for visualization and quantification of 4D medical images

The object of this study was to develop and implement a fast and interactive tool for the visualization and analysis of 4D (3D+time) medical data on conventional computers and designed for a clinical environment where speed and simplicity are no secondary factor to accuracy. The tool was developed utilizing different programming languages to exploit the best characteristics from each and taking into account portability, integration, modularity, efficiency, computational speed, and graphical capabilities of the software. The quantification algorithms we implemented for the data analysis and for the estimation of quantitative parameters are based on partial differential equations and level set models. The result was a user-friendly, fast and easy-to-use tool that provides reliable and consistent 3D and dynamic data visualization and quantitative functionality parameters extraction without expensive workstation or dedicated hardware. Therefore, it potentially represents a novel tool to be used for visualization and processing of complex anatomies, acquired with different imaging systems, in clinical practice.

Explorative and dynamic visualization of data in virtual reality

A software system has been developed for the study of dynamic glyph visualizations in the context of Visual Data Mining in Virtual Reality. The system uses parallel processing to calculate data visualizations in real-time, with real-time interaction and dynamic changes to the view. The system allows morphing between different visualizations, the use of dynamic features like “vibrations” and “rotations” of thousands of objects individually, and dynamic visualization, where the influence of any variable of a dataset with a “reasonable” distribution, can be shown as a dynamic development. It appears that these facilities for dynamic data visualization have a very promising potential, but their optimal use will depend on further developments in the context of their individual practical application.

Geometry of gene expression dynamics

A gene expression trajectory moves through a high dimensional space where each axis represents the mRNA abundance of a different gene. Genome wide gene expression has a dynamic structure, especially in studies of development and temporal response. Both visualization and analyses of such data require an explicit attention to the temporal structure. Using three cell cycle trajectories from Saccharomyces cerevisiae to illustrate, we present several techniques which reveal the geometry of the data. We import phase-delay time plots from chaotic systems theory as a dynamic data visualization device and show how these plots capture important aspects of the trajectories. We construct an objective function to find an optimal two-dimensional projection of the cell cycle, demonstrate that the system returns to this plane after three different initial perturbations, and explore the conditions under which this geometric approach outperforms standard approaches such as singular value decomposition and Fourier analysis. Finally, we show how a geometric analysis can isolate distinct parts of the trajectories, in this case the initial perturbation versus the cell cycle. junhyong.kim@yale.edu