Feature-preserving volume data reduction and focus+context visualization

The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.     

Feature aligned volume manipulation for illustration and visualization

In this paper we describe a GPU-based technique for creating illustrative visualization through interactive manipulation of volumetric models. It is partly inspired by medical illustrations, where it is common to depict cuts and deformation in order to provide a better understanding of anatomical and biological structures or surgical processes, and partly motivated by the need for a real-time solution that supports the specification and visualization of such illustrative manipulation. We propose two new feature-aligned techniques, namely surface alignment and segment alignment, and compare them with the axis-aligned techniques which was reported in previous work on volume manipulation. We also present a mechanism for defining features using texture volumes, and methods for computing correct normals for the deformed volume in respect to different alignments. We describe a GPU-based implementation to achieve real-time performance of the techniques and a collection of manipulation operators including peelers, retractors, pliers and dilators which are adaptations of the metaphors and tools used in surgical procedures and medical illustrations. Our approach is directly applicable in medical and biological illustration, and we demonstrate how it works as an interactive tool for focus+context visualization, as well as a generic technique for volume graphics.     

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.     

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.     

Haptics-based dynamic implicit solid modeling

We systematically present a novel, interactive solid modeling framework, haptics-based dynamic implicit solid modeling, which is founded upon volumetric implicit functions and powerful physics-based modeling. In particular, we augment our modeling framework with a haptic mechanism in order to take advantage of additional realism associated with a 3D haptic interface. Our dynamic implicit solids are semialgebraic sets of volumetric implicit functions and are governed by the principles of dynamics, hence responding to sculpting forces in a natural and predictable manner. In order to directly manipulate existing volumetric data sets as well as point clouds, we develop a hierarchical fitting algorithm to reconstruct and represent discrete data sets using our continuous implicit functions, which permit users to further design and edit those existing 3D models in real-time using a large variety of haptic and geometric toolkits, and visualize their interactive deformation at arbitrary resolution. The additional geometric and physical constraints afford more sophisticated control of the dynamic implicit solids. The versatility of our dynamic implicit modeling enables the user to easily modify both the geometry and the topology of modeled objects, while the inherent physical properties can offer an intuitive haptic interface for direct manipulation with force feedback.     

Interactive transfer function design based on editing direct volume rendered images

Direct volume rendered images (DVRIs) have been widely used to reveal structures in volumetric data. However, DVRIs generated by many volume visualization techniques can only partially satisfy users' demands. In this paper, we propose a framework for editing DVRIs, which can also be used for interactive transfer function (TF) design. Our approach allows users to fuse multiple features in distinct DVRIs into a comprehensive one, to blend two DVRIs, and/or to delete features in a DVRI. We further present how these editing operations can generate smooth animations for focus + context visualization. Experimental results on some real volumetric data demonstrate the effectiveness of our method.     

Interactive visualization of three-dimensional vector fields with flexible appearance control

In this paper, we present an interactive texture-based algorithm for visualizing three-dimensional steady and unsteady vector fields. The goal of the algorithm is to provide a general volume rendering framework allowing the user to compute three-dimensional flow textures interactively and to modify the appearance of the visualization on the fly. To achieve our goal, we decouple the visualization pipeline into two disjoint stages. First, flow lines are generated from the 3D vector data. Various geometric properties of the flow paths are extracted and converted into a volumetric form using a hardware-assisted slice sweeping algorithm. In the second phase of the algorithm, the attributes stored in the volume are used as texture coordinates to look up an appearance texture to generate both informative and aesthetic representations of the vector field. Our algorithm allows the user to interactively navigate through different regions of interest in the underlying field and experiment with various appearance textures. With our algorithm, visualizations with enhanced structural perception using various visual cues can be rendered in real time. A myriad of existing geometry-based and texture-based visualization techniques can also be emulated.     

Illustrative uncertainty visualization of DTI fiber pathways

Diffusion Tensor Imaging (DTI) and fiber tracking provide unique insight into the 3D structure of fibrous tissues in the brain. However, the output of fiber tracking contains a significant amount of uncertainty accumulated in the various steps of the processing pipeline. Existing DTI visualization methods do not present these uncertainties to the end-user. This creates a false impression of precision and accuracy that can have serious consequences in applications that rely heavily on risk assessment and decision-making, such as neurosurgery. On the other hand, adding uncertainty to an already complex visualization can easily lead to information overload and visual clutter. In this work, we propose Illustrative Confidence Intervals to reduce the complexity of the visualization and present only those aspects of uncertainty that are of interest to the user. We look specifically at the uncertainty in fiber shape due to noise and modeling errors. To demonstrate the flexibility of our framework, we compute this uncertainty in two different ways, based on (1) fiber distance and (2) the probability of a fiber connection between two brain regions. We provide the user with interactive tools to define multiple confidence intervals, specify visual styles and explore the uncertainty with a Focus+Context approach. Finally, we have conducted a user evaluation with three neurosurgeons to evaluate the added value of our visualization.     

Texture-based feature tracking for effective time-varying data visualization

Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.     

Direct Visualization of Deformation in Volumes

Deformation is a topic of interest in many disciplines. In particular in medical research, deformations of surfaces and even entire volumetric structures are of interest. Clear visualization of such deformations can lead to important insight into growth processes and progression of disease.
We present new techniques for direct focus+context visualization of deformation fields representing transformations between pairs of volumetric datasets. Typically, such fields are computed by performing a non-rigid registration between two data volumes. Our visualization is based on direct volume rendering and uses the GPU to compute and interactively visualize features of these deformation fields in real-time. We integrate visualization of the deformation field with visualization of the scalar volume affected by the deformations. Furthermore, we present a novel use of texturing in volume rendered visualizations to show additional properties of the vector field on surfaces in the volume.     

Interactive deformation and cutting simulation directly using patient‐specific volumetric images

This paper systematically advocates an interactive volumetric image manipulation framework, which can enable the rapid deployment and instant utility of patient‐specific medical images in virtual surgery simulation while requiring little user involvement. We seamlessly integrate multiple technical elements to synchronously accommodate physics‐plausible simulation and high‐fidelity anatomical structures visualization. Given a volumetric image, in a user‐transparent way, we build a proxy to represent the geometrical structure and encode its physical state without the need of explicit 3‐D reconstruction. On the basis of the dynamic update of the proxy, we simulate large‐scale deformation, arbitrary cutting, and accompanying collision response driven by a non‐linear finite element method. By resorting to the upsampling of the sparse displacement field resulted from non‐linear finite element simulation, the cut/deformed volumetric image can evolve naturally and serves as a time‐varying 3‐D texture to expedite direct volume rendering. Moreover, our entire framework is built upon CUDA (Beihang University, Beijing, China) and thus can achieve interactive performance even on a commodity laptop. The implementation details, timing statistics, and physical behavior measurements have shown its practicality, efficiency, and robustness. Copyright © 2013 John Wiley & Sons, Ltd.     

Interactive multilevel focus+context visualization framework

In this article, we present the construction of an interactive multilevel focus+context visualization framework for the navigation and exploration of large-scale 2D and 3D images. The presented framework utilizes a balanced multiresolution technique supported by a balanced wavelet transform (BWT). It extends the mode of focus+context visualization, where spatially separate magnification of regions of interest (ROIs) is performed, as opposed to in-place magnification. Each resulting visualization scenario resembles a tree structure, where the root constitutes the main context, each non-root internal node plays the dual roles of both focus and context, and each leaf solely represents a focus. Our developed prototype supports interactive manipulation of the visualization hierarchy, such as addition and deletion of ROIs and desired changes in their resolutions at any level of the hierarchy on the fly. We describe the underlying data structure efficiently support such operations. Changes in the spatial locations of query windows defining the ROIs trigger on-demand reconstruction queries. We explain in detail how to efficiently process such reconstruction queries within the hierarchy of details (wavelet coefficients) contained in the BWT in order to ensure real-time feedback. As the BWT need only be constructed once in a preprocessing phase on the server-side and robust on-demand reconstruction queries require minimal data communication overhead, our presented framework is a suitable candidate for efficient web-based visualization of complex large-scale imagery. We also discuss the performance characteristics of our proposed framework from various aspects, such as time and space complexities and achieved frame rates.     

A model for volume lighting and modeling

Direct volume rendering is a commonly used technique in visualization applications. Many of these applications require sophisticated shading models to capture subtle lighting effects and characteristics of volumetric data and materials. For many volumes, homogeneous regions pose problems for typical gradient-based surface shading. Many common objects and natural phenomena exhibit visual quality that cannot be captured using simple lighting models or cannot be solved at interactive rates using more sophisticated methods. We present a simple yet effective interactive shading model which captures volumetric light attenuation effects that incorporates volumetric shadows, an approximation to phase functions, an approximation to forward scattering, and chromatic attenuation that provides the subtle appearance of translucency. We also present a technique for volume displacement or perturbation that allows realistic interactive modeling of high frequency detail for both real and synthetic volumetric data.     

Texture-based visualization of unsteady 3D flow by real-time advection and volumetric illumination

This paper presents an interactive technique for the dense texture-based visualization of unsteady 3D flow, taking into account issues of computational efficiency and visual perception. High efficiency is achieved by a 3D graphics processing unit (GPU)-based texture advection mechanism that implements logical 3D grid structures by physical memory in the form of 2D textures. This approach results in fast read and write access to physical memory, independent of GPU architecture. Slice-based direct volume rendering is used for the final display. We investigate two alternative methods for the volumetric illumination of the result of texture advection: First, gradient-based illumination that employs a real-time computation of gradients, and, second, line-based lighting based on illumination in codimension 2. In addition to the Phong model, perception-guided rendering methods are considered, such as cool/warm shading, halo rendering, or color-based depth cueing. The problems of clutter and occlusion are addressed by supporting a volumetric importance function that enhances features of the flow and reduces visual complexity in less interesting regions. GPU implementation aspects, performance measurements, and a discussion of results are included to demonstrate our visualization approach.     

Interactive focus + context medical data exploration and editing

Volumetric datasets are increasingly used in medical applications. In many of these applications, visualization and interaction is generally performed on cross‐sectional two‐dimensional (2D) views of three‐dimensional (3D) imaging modalities. Displaying 3D volumetric medical datasets on traditional 2D screens can present problems such as occlusion and information overload, especially when multiple data sources are present. Displaying desired information while showing the relationship to the rest of the dataset(s) can be challenging. In this paper, we present an interactive focus + context visualization approach that uses the volumetric Magic Lens interaction paradigm. We propose to use the Magic Lens as a volumetric brush to perform volume editing tasks, therefore combining data exploration with volumetric editing. Polygon‐assisted ray casting methods are used for real‐time rendering and editing frame rates, while providing compact storage of editing states for undo/redo operations. We discuss the application of our methods to radiation therapy, which is an important cancer treatment modality. We envision that this approach will improve the treatment planning process by improving the therapists' understanding of information from various sources and will help identify if the alignment of the patient in the treatment room coincides with the prepared treatment plan. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessing the use of slicing-based visualizing techniques on the understanding of large metamodels

ContextMetamodels are cornerstones of various metamodeling activities. Such activities consist of, for instance, transforming models into code or comparing metamodels. These activities thus require a good understanding of a metamodel and/or its parts. Current metamodel editing tools are based on standard interactive visualization features, such as physical zooms.ObjectiveHowever, as soon as metamodels become large, navigating through large metamodels becomes a tedious task that hinders their understanding. So, a real need to support metamodel comprehension appears.MethodIn this work we promote the use of model slicing techniques to build interactive visualization tools for metamodels. Model slicing is a model comprehension technique inspired by program slicing. We show how the use of Kompren, a domain-specific language for defining model slicers, can ease the development of such interactive visualization features.ResultsWe specifically make four main contributions. First, the proposed interactive visualization techniques permit users to focus on metamodel elements of interest, which aims at improving the understandability. Second, these proposed techniques are developed based on model slicing, a model comprehension technique that involves extracting a subset of model elements of interest. Third, we develop a metamodel visualizer, called Explen, embedding the proposed interactive visualization techniques. Fourth, we conducted experiments. showing that Explen significantly outperforms EcoreTools, in terms of time, correctness, and navigation effort, on metamodeling tasks.ConclusionThe results of the experiments, in favor of Explen, show that improving metamodel understanding can be done using slicing-based interactive navigation features.     

Relation-aware volume exploration pipeline

Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization.     

Large steps in GPU-based deformable bodies simulation

The interactive deformation and visualization of volumetric objects is still a challenging problem for many application areas. We present a novel integrated system which implements physically-based deformation and volume visualization of tetrahedral meshes on modern graphics hardware by exploiting the last features of vertex and fragment shaders.We achieve fast and stable deformation of tetrahedral meshes by means of a GPU-based implicit solver and present a hardware-based single-pass raycaster for deformed tetrahedral meshes. Thus, direct visualization of the inner structures of the deformed mesh is possible, while keeping the data on the graphics hardware throughout the entire simulation.     

Geodesic distance-weighted shape vector image diffusion

This paper presents a novel and efficient surface matching and visualization framework through the geodesic distance-weighted shape vector image diffusion. Based on conformal geometry, our approach can uniquely map a 3D surface to a canonical rectangular domain and encode the shape characteristics (e.g., mean curvatures and conformal factors) of the surface in the 2D domain to construct a geodesic distance-weighted shape vector image, where the distances between sampling pixels are not uniform but the actual geodesic distances on the manifold. Through the novel geodesic distance-weighted shape vector image diffusion presented in this paper, we can create a multiscale diffusion space, in which the cross-scale extrema can be detected as the robust geometric features for the matching and registration of surfaces. Therefore, statistical analysis and visualization of surface properties across subjects become readily available. The experiments on scanned surface models show that our method is very robust for feature extraction and surface matching even under noise and resolution change. We have also applied the framework on the real 3D human neocortical surfaces, and demonstrated the excellent performance of our approach in statistical analysis and integrated visualization of the multimodality volumetric data over the shape vector image.