Extensions of parallel coordinates for interactive exploration of large multi-timepoint data sets

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.     

Research issues in volume visualization

Volume visualization is a method of extracting meaningful information from volumetric data sets through the use of interactive graphics and imaging. It addresses the representation, manipulation, and rendering of volumetric data sets, providing mechanisms for peering into structures and understanding their complexity and dynamics. Typically, the data set is represented as a 3D regular grid of volume elements (voxels) and stored in a volume buffer (also called a cubic frame buffer), which is a large 3D array of voxels. However, data is often defined at scattered or irregular locations that require using alternative representations and rendering algorithms. There are eight major research issues in volume visualization: volume graphics, volume rendering, transform coding of volume data, scattered data, enriching volumes with knowledge, segmentation, real-time rendering and parallelism, and special purpose hardware     

Hybrid Data Visualization Based on Depth Complexity Histogram Analysis

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

Interactive Volume Rendering with Dynamic Ambient Occlusion and Color Bleeding

We propose a method for rendering volumetric data sets at interactive frame rates while supporting dynamic ambient occlusion as well as an approximation to color bleeding. In contrast to ambient occlusion approaches for polygonal data, techniques for volumetric data sets have to face additional challenges, since by changing rendering parameters, such as the transfer function or the thresholding, the structure of the data set and thus the light interactions may vary drastically. Therefore, during a preprocessing step which is independent of the rendering parameters we capture light interactions for all combinations of structures extractable from a volumetric data set. In order to compute the light interactions between the different structures, we combine this preprocessed information during rendering based on the rendering parameters defined interactively by the user. Thus our method supports interactive exploration of a volumetric data set but still gives the user control over the most important rendering parameters. For instance, if the user alters the transfer function to extract different structures from a volumetric data set the light interactions between the extracted structures are captured in the rendering while still allowing interactive frame rates. Compared to known local illumination models for volume rendering our method does not introduce any substantial rendering overhead and can be integrated easily into existing volume rendering applications. In this paper we will explain our approach, discuss the implications for interactive volume rendering and present the achieved results.     

Spray rendering

Spray rendering provides a framework for creating and experimenting with different visualization techniques. The name spray rendering is derived from the metaphor of using a virtual spray can to paint data sets. Varying the type of paint in the can highlights data in different ways. Spray rendering is not limited to the paint metaphor, however. Other useful metaphors include a flashlight and a probe. Thus, spray rendering refers to the localized nature of the visualization algorithms and the manner in which the algorithms are sent to the data sets. We gain several advantages by looking at visualization algorithms in this way, including extensibility, grid independence, and ability to handle large data sets. This article presents the benefits, conceptual design, issues and directions of spray rendering     

Volume splitting and its applications

Splitting a volumetric object is a useful operation in volume graphics and its applications, but is not widely supported by existing systems for volume-based modeling and rendering. In this paper, we present an investigation into two main algorithmic approaches, namely, explicit and implicit splitting, for modeling and rendering splitting actions. We consider a generalized notion based on scalar fields, which encompasses discrete specifications (e.g., volume data sets) as well as procedural specifications (e.g., hypertextures) of volumetric objects. We examine the correctness, effectiveness, efficiency, and deficiencies of each approach in specifying and controlling a spatial and temporal specification of splitting. We propose methods for implementing these approaches and for overcoming their deficiencies. We present a modeling tool for creating specifications of splitting functions, and describe the use of volume scene graphs for facilitating direct rendering of volume splitting. We demonstrate the use of these approaches with examples of volume visualization, medical illustration, volume animation, and special effects     

Visualizing and segmenting large volumetric data sets

Current systems for segmenting and visualizing volumetric data sets characteristically require the user to possess a technical sophistication in volume visualization techniques, thus restricting the potential audience of users. As large volumetric data sets become more common, segmentation and visualization tools need to deemphasize the technical aspects of visualization and let users exploit their content knowledge of the data set. This proves especially critical in an educational setting. In anatomical education, data sets such as the Visible Human Project provide significant learning opportunities, but students must have tools that let them apply, refine, and build on their anatomical knowledge without technical obstacles. I describe a software environment that uses immersive virtual reality technology to let users immediately apply their expert knowledge to exploring and visualizing volumetric data sets     

Visualization of cellular and microvascular relationships

Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.     

Parallel Shear-Warp Factorization Volume Rendering Using Efficient 1-D and 2-D Partitioning Schemes for Distributed Memory Multicomputers

3-D data visualization is very useful for medical imaging and computational fluid dynamics. Volume rendering can be used to exhibit the shape and volumetric properties of 3-D objects. However, volume rendering requires a considerable amount of time to process the large volume of data. To deliver the necessary rendering rates, parallel hardware architectures such as distributed memory multicomputers offer viable solutions. The challenge is to design efficient parallel algorithms that utilize the hardware parallelism effectively. In this paper, we present two efficient parallel volume rendering algorithms, the 1D-partition and 2D-partition methods, based on the shear-warp factorization for distributed memory multicomputers. The 1D-partition method has a performance bound on the size of the volume data. If the number of processors is less than a threshold, the 1D-partition method can deliver a good rendering rate. If the number of processors is over a threshold, the 2D-partition method can be used. To evaluate the performance of these two algorithms, we implemented the proposed methods along with the slice data partitioning, volume data partitioning, and sheared volume data partitioning methods on an IBM SP2 parallel machine. Six volume data sets were used as the test samples. The experimental results show that the proposed methods outperform other compatible algorithms for all test samples. When the number of processors is over a threshold, the experimental results also demonstrate that the 2D-partition method is better than the 1D-partition method.     

Two-level volume rendering

Presents a two-level approach for volume rendering, which allows for selectively using different rendering techniques for different subsets of a 3D data set. Different structures within the data set are rendered locally on an object-by-object basis by either direct volume rendering (DVR), maximum-intensity projection (MIP), surface rendering, value integration (X-ray-like images) or non-photorealistic rendering (NPR). All the results of subsequent object renderings are combined globally in a merging step (usually compositing in our case). This allows us to selectively choose the most suitable technique for depicting each object within the data while keeping the amount of information contained in the image at a reasonable level. This is especially useful when inner structures should be visualized together with semi-transparent outer parts, similar to the focus+context approach known from information visualization. We also present an implementation of our approach which allows us to explore volumetric data using two-level rendering at interactive frame rates     

High-quality cardiac image dynamic visualization with feature enhancement and virtual surgical tool inclusion

Traditional approaches for rendering segmented volumetric data sets usually deliver unsatisfactory results, such as insufficient frame rate, low image quality, and intermixing artifacts. In this paper, we introduce a novel “color encoding” technique, based on graphics processing unit (GPU) accelerated raycasting and post-color attenuated classification, to address this problem. The result is an algorithm that can generate artifact-free dynamic volumetric images in real time. Next, we present a pre-integrated volume shading algorithm to reduce graphics memory requirements and computational cost when compared to traditional shading methods. We also present a normal-adjustment technique to improve image quality at clipped planes. Furthermore, we propose a new algorithm for color and depth texture indexing that permits virtual solid objects, such as surgical tools, to be manipulated within the dynamically rendered volumetric cardiac images in real time. Finally, all these techniques are combined within an environment that permits real-time visualization, enhancement, and manipulation of dynamic cardiac data sets.     

Volumetric video compression for interactive playback

We develop a volumetric video system which supports interactive browsing of compressed time-varying volumetric features (significant isosurfaces and interval volumes). Since the size of even one volumetric frame in a time-varying 3D data set is very large, transmission and on-line reconstruction are the main bottlenecks for interactive remote visualization of time-varying volume and surface data. We describe a compression scheme for encoding time-varying volumetric features in a unified way, which allows for on-line reconstruction and rendering. To increase the run-time decompression speed and compression ratio, we decompose the volume into small blocks and encode only the significant blocks that contribute to the isosurfaces and interval volumes. The results show that our compression scheme achieves high compression ratio with fast reconstruction, which is effective for interactive client-side rendering of time-varying volumetric features.     

Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization

In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule‐enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.     

基于PC硬件的大规模数据场快速可视化方法

科学可视化技术在众多领域具有十分广泛的应用,然而直接体绘制技术却有着计算量大、计算时间长的缺点,在普通的PC机上很难实现对大规模数据的实时交互绘制。目前的三维可视化系统通常需要架构在高端的图形工作站或转用计算机上。随着计算机软硬件技术的发展,普通的PC机图形处理器GPU(Graphic Processing Unit)具有了可编程功能。正是借助GPU的可编程功能及其强大的并行处理能力,研究并实现了一种基于普通PC硬件的体会之方法。最后应用该方法对工业、医学等体数据进行可视化,结果证明该方法可以在普通PC上实现较大规模数据的快速可视化。     

体数据内部结构绘制技术及其在3维医学图像分析中的应用

首先基于集合论方法对体数据内部结构进行分析(内部结构是指被外部物体遮挡或隐藏而不能从外部直接看到的物体或结构),从而给出了内部结构绘制技术的功能要求。基于此功能要求,提出了基于内部结构区域的体绘制技术来分析体数据内部结构的方法。基于内部结构区域的体绘制技术之所以能够分析体数据内部结构是因为该方法把体数据分为两个子集:内部结构区域(或称局部区域)和周围区域。内部结构区域提供了深入体数据内部的机制,通过对内部结构区域的交互,用户可以分析体数据内部的任意区域,从而达到对体数据内部结构和整个体数据的综合理解。该方法还可对内部结构进行量化分析从而提高用户分析体数据的质量。最后,该方法用于分析3维医学图像以显示其在实际应用中的强大功能。实验结果表明,该方法对3维医学图像中的内部结构可以有效地进行定性和定量分析。     

Style Transfer Functions for Illustrative Volume Rendering

Illustrative volume visualization frequently employs non-photorealistic rendering techniques to enhance important features or to suppress unwanted details. However, it is difficult to integrate multiple non-photorealistic rendering approaches into a single framework due to great differences in the individual methods and their parameters. In this paper, we present the concept of style transfer functions. Our approach enables flexible data-driven illumination which goes beyond using the transfer function to just assign colors and opacities. An image-based lighting model uses sphere maps to represent non-photorealistic rendering styles. Style transfer functions allow us to combine a multitude of different shading styles in a single rendering. We extend this concept with a technique for curvature-controlled style contours and an illustrative transparency model. Our implementation of the presented methods allows interactive generation of high-quality volumetric illustrations.     

Interactive level-of-detail selection using image-based quality metric for large volume visualization

For large volume visualization, an image-based quality metric is difficult to incorporate for level-of-detail selection and rendering without sacrificing the interactivity. This is because it is usually time-consuming to update view-dependent information as well as to adjust to transfer function changes. In this paper, we introduce an image-based level-of-detail selection algorithm for interactive visualization of large volumetric data. The design of our quality metric is based on an efficient way to evaluate the contribution of multiresolution data blocks to the final image. To ensure real-time update of the quality metric and interactive level-of-detail decisions, we propose a summary table scheme in response to runtime transfer function changes and a GPU-based solution for visibility estimation. Experimental results on large scientific and medical data sets demonstrate the effectiveness and efficiency of our algorithm     

Minimally immersive flow visualization

This paper describes a minimally immersive interactive system for flow visualization of multivariate volumetric data. The system, SFA, uses perceptually motivated rendering to increase the quantity and clarity of information perceived. Proprioception, stereopsis, perceptually motivated shape visualization, and three-dimensional interaction are combined in SFA to allow the three-dimensional volumetric visualization, manipulation, navigation, and analysis of multivariate, time-varying flow data     

A practical approach to spectral volume rendering

To make a spectral representation of color practicable for volume rendering, a new low-dimensional subspace method is used to act as the carrier of spectral information. With that model, spectral light material interaction can be integrated into existing volume rendering methods at almost no penalty. In addition, slow rendering methods can profit from the new technique of postillumination-generating spectral images in real-time for arbitrary light spectra under a fixed viewpoint. Thus, the capability of spectral rendering to create distinct impressions of a scene under different lighting conditions is established as a method of real-time interaction. Although we use an achromatic opacity in our rendering, we show how spectral rendering permits different data set features to be emphasized or hidden as long as they have not been entirely obscured. The use of postillumination is an order of magnitude faster than changing the transfer function and repeating the projection step. To put the user in control of the spectral visualization, we devise a new widget, a "light-dial", for interactively changing the illumination and include a usability study of this new light space exploration tool. Applied to spectral transfer functions, different lights bring out or hide specific qualities of the data. In conjunction with postillumination, this provides a new means for preparing data for visualization and forms a new degree of freedom for guided exploration of volumetric data sets