Engaging mass audiences with crucial societal issues, such as cli-mate change, can be provided through interactive exhibits designed around the paradigm of exploranation. We present example inter-active installations in the newly founded Wadstr¨oms Exploranation Laboratory that explain various aspects of climate change while allowing public participants to explore the real scientific data. We describe how effects and causes of climate change can be communi-cated by two of the installations that allow for interactive opportuni-ties to explore the underlying data while gaining insight into climate change sources and effects. We close with implications for future work on exploranation as an emerging visualization pedagogy in public spaces.

The extended reality market has rapidly grown with a wide range of products for not only Virtual Reality applications, but also for advanced and multiple forms of Augmented Reality. In this paper we review the currently available complete solutions for Augmented Reality, divided into the primary display techniques used: Video See-through, using cameras to capture the real world subsequently presented with virtual overlays on a handheld or headworn display, Optical See-through, using semi-transparent display to allow real world view together with the virtual augmentations, and Projection-based AR or Spatial AR, the use of projectors to display augmentations directly on top of surfaces in the room.  First potential products were found using popular Internet search engines, after which products that are not complete solutions or not commercially available were filtered out. We present the different products together with a description of their presented or studied characteristics, and of their accompanying software solutions.

While the usefulness of 3D visualizations has been shown for a range of clinical applications such as treatment planning it still had difficulties in being adopted in widespread clinical practice. This chapter describes how multi-touch surfaces with patient-specific data have contributed to breaking this barrier, paving the way for adoption into clinical practice and, at the same time, also found widespread use in educational settings and in communication of science to the general public. The key element identified for this adoption is the string of steps found in the full imaging chain, which will be described as an introduction to the topic in this chapter. Emphasis in the chapter is, however, visualization aspects, e.g., intuitive interaction with patient-specific data captured with the latest high speed and high-quality imaging modalities. A necessary starting point for this discussion is the foundations of and state-of-the-art in volumetric rendering, which form the basis for the underlying theory part of the chapter. The chapter presents two use cases. One case is focusing on the use of multi-touch in medical education and the other is focusing on the use of touch surfaces at public venues, such as science centers and museums. 

Frequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transfer function (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly in low-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to the area of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of local frequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allows us to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplify the exploration step. We propose three approaches for data exploration which we illustrate with several visualization cases highlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstrate the power of the method on selected datasets.

Visualization has evolved into a mature scientific field and it has also become widely accepted as a standard approach in diverse fields, including physics, life sciences, and business intelligence. However, despite its successful development, there are still many open research questions that require customized implementations in order to explore and establish concepts, and to perform experiments and take measurements. Many methods and tools have been developed and published but most are stand-alone prototypes and have not reached a mature state that can be used in a reliable manner by collaborating domain scientists or a wider audience. In this study, we discuss the challenges, solutions, and open research questions that affect the development of sophisticated, relevant, and novel scientific visualization solutions with minimum overheads. We summarize and discuss the results of a recent National Institute of Informatics Shonan seminar on these topics. (C) 2020 Elsevier Ltd. All rights reserved.

Results of planetary mapping are often shared openly for use in scientific research and mission planning. In its raw format, however, the data is not accessible to non-experts due to the difficulty in grasping the context and the intricate acquisition process. We present work on tailoring and integration of multiple data processing and visualization methods to interactively contextualize geospatial surface data of celestial bodies for use in science communication. As our approach handles dynamic data sources, streamed from online repositories, we are significantly shortening the time between discovery and dissemination of data and results. We describe the image acquisition pipeline, the pre-processing steps to derive a 2.5D terrain, and a chunked level-of-detail, out-of-core rendering approach to enable interactive exploration of global maps and high-resolution digital terrain models. The results are demonstrated for three different celestial bodies. The first case addresses high-resolution map data on the surface of Mars. A second case is showing dynamic processes. such as concurrent weather conditions on Earth that require temporal datasets. As a final example we use data from the New Horizons spacecraft which acquired images during a single flyby of Pluto. We visualize the acquisition process as well as the resulting surface data. Our work has been implemented in the OpenSpace software [8], which enables interactive presentations in a range of environments such as immersive dome theaters. interactive touch tables. and virtual reality headsets.

In this paper, we tackle the challenge of effective Transfer Function (TF) design for Direct Volume Rendering (DVR) of full-color datasets. We propose a novel TF design toolbox based on color similarity which is used to adjust opacity as well as replacing colors. We show that both CIE L*u*v* chromaticity and the chroma component of YCbCr are equally suited as underlying color space for the TF widgets. In order to maximize the area utilized in the TF editor, we renormalize the color space based on the histogram of the dataset. Thereby, colors representing a higher share of the dataset are depicted more prominently, thus providing a higher sensitivity for fine-tuning TF widgets. The applicability of our TF design toolbox is demonstrated by volume ray casting challenging full-color volume data including the visible male cryosection dataset and examples from 3D histology.

BY COMBINING VISUALIZATION techniques with interactive multi-touch tables and intuitive user interfaces, visitors to museums and science centers can conduct self-guided tours of large volumetric image data. In an interactive learning experience, visitors become the explorers of otherwise invisible interiors of unique artifacts and subjects. Here, we take as our starting point the state of the art in scanning technologies, then discuss the latest research on high-quality interactive volume rendering and how it can be tailored to meet the specific demands

A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.

Medical imaging plays a central role in a vast range of healthcare practices. While the usefulness of 3D visualizations is well known, the adoption of such technology has previously been limited in many medical areas. This paper, awarded the Dirk Bartz Prize for Visual Computing in Medicine 2015, describes the development of a medical multi-touch visualization table that successfully has reached its aim to bring 3D visualization to a wider clinical audience. The descriptions summarize the targeted clinical scenarios, the key characteristics of the system, and the user feedback obtained.

There are numerous advantages described of how imaging technology can support forensic examinations. However, postmortem examinations of bodies are mainly performed to address demands which differ from those of traditional clinical image processing. This needs to be kept in mind when gathering information from image data sets for forensic purposes. To support radiologists and forensic clinicians using Virtual Autopsy technologies, an initial workflow study regarding post-mortem imaging has been performed, aiming to receive an improved understanding of how Virtual Autopsy workstations, image data sets and processes can be adjusted to support and improve conventional autopsies. This paper presents potential impacts and a current forensic Virtual Autopsy workflow aiming to form a foundation for collaborative procedures that increase the value of Virtual Autopsy. The workflow study will provide an increased and mutual understanding of involved professionals. In addition, insight into future forensic workflows based on demands from both forensic and radiologist perspectives bring visualization and medical informatics researchers together to develop and improve the technology and software needed.

We present a novel approach to interactive and concurrent volume visualization of functional Magnetic Resonance Imaging (fMRI). While the patient is in the scanner, data is extracted in real-time using state-of-the-art signal processing techniques. The fMRI signal is treated as light emission when rendering a patient-specific high resolution reference MRI volume, obtained at the beginning of the experiment. As a result, the brain glows and emits light from active regions. The low resolution fMRI signal is thus effectively fused with the reference brain with the current transfer function settings yielding an effective focus and context visualization. The delay from a change in the fMRI signal to the visualization is approximately 2 seconds. The advantage of our method over standard 2D slice based methods is shown in a user study. We demonstrate our technique through experiments providing interactive visualization to the fMRI operator and also to the test subject in the scanner through a head mounted display.

A single digital pathology image can occupy over 10 gigabytes of hard disk space, rendering it difficult to store, analyze, and transmit. Though image compression provides a means of reducing the storage requirement, its effects on computer-aided diagnosis (CAD) and pathologist performance are not yet clear. In this work we assess the impact of compression on the ability of a CAD system to detect carcinoma of the prostate (CaP) on histological sections. The CAD algorithm proceeds as follows: Glands in the tissue are segmented using a region-growing algorithm, and the size of each gland is extracted. A Markov prior (specifically, a probabilistic pairwise Markov model) is employed to encourage nearby glands to share the same class (i.e. cancerous or non-cancerous). Finally, cancerous glands are aggregated into continuous regions using a distancehull algorithm. We trained the CAD system on 28 images of wholemount histology (WMH) and evaluated performance on 12 images compressed at 14 different compression ratios (a total of 168 experiments) using JPEG2000. Algorithm performance (measured using the under the receiver operating characteristic curves) remains relatively constant for compression ratios up to1 :256, beyond which performance degrades precipitously. For completeness we also have an expert pathologist view a randomly-selected set of compressed images from one of the whole mount studies and assign a confidence measure as to their diagnostic fidelity. Pathologist confidence declined with increasing compression ratio as the information necessary to diagnose the sample was lost, dropping from 100% confidence at ratio 1:64 to 0% at ratio 1:8192.

This paper presents a novel technique to efficiently compute illumination for Direct Volume Rendering using a local approximation of ambient occlusion to integrate the intensity of incident light for each voxel. An advantage with this local approach is that fully shadowed regions are avoided, a desirable feature in many applications of volume rendering such as medical visualization.

Additional transfer function interactions are also presented, for instance, to highlight specific structures with luminous tissue effects and create an improved context for semitransparent tissues with a separate absorption control for the illumination settings. Multiresolution volume management and GPU-based computation are used to accelerate the calculations and support large data sets. The scheme yields interactive frame rates with an adaptive sampling approach for incrementally refined illumination under arbitrary transfer function changes. The illumination effects can give a better understanding of the shape and density of tissues and so has the potential to increase the diagnostic value of medical volume rendering. Since the proposed method is gradient-free, it is especially beneficial at the borders of clip planes, where gradients are undefined, and for noisy data sets.

The most common representation of volumetric models is a regular grid of cubical voxels with one value each, from which a smooth scalar field is reconstructed. However, common real-world situations include cases in which volumes represent physical objects with well defined boundaries separating different materials, giving rise to models with quasi-impulsive gradient fields. In our split-voxel representation, we replace blocks of N 3 voxels by one single voxel that is split by a feature plane into two regions with constant values. This representation has little overhead over storing precomputed gradients, and has the advantage that feature planes provide minimal geometric information about the underlying volume regions that can be effectively exploited for volume rendering. We show how to convert a standard mono-resolution representation into a out-of-core multiresolution structure, both for labeled and continuous scalar volumes. We also show how to interactively explore the models using a multiresolution GPU raycasting framework. The technique supports real-time transfer function manipulation and proves particularly useful for fast multiresolution rendering, since accurate silhouettes are preserved even at very coarse levels of detail.

In many applications of Direct Volume Rendering (DVR) the importance of a certain material or feature is highly dependent on its relative spatial location. For instance, in the medical diagnostic procedure, the patients symptoms often lead to specification of features, tissues and organs of particular interest. One such example is pockets of gas which, if found inside the body at abnormal locations, are a crucial part of a diagnostic visualization. This paper presents an approach that enhances DVR transfer function design with spatial localization based on user specified material dependencies. Semantic expressions are used to define conditions based on relations between different materials, such as only render iodine uptake when close to liver. The underlying methods rely on estimations of material distributions which are acquired by weighing local neighborhoods of the data against approximations of material likelihood functions. This information is encoded and used to influence rendering according to the users specifications. The result is improved focus on important features by allowing the user to suppress spatially less-important data. In line with requirements from actual clinical DVR practice, the methods do not require explicit material segmentation that would be impossible or prohibitively time-consuming to achieve in most real cases. The scheme scales well to higher dimensions which accounts for multi-dimensional transfer functions and multivariate data. Dual-Energy Computed Tomography, an important new modality in radiology, is used to demonstrate this scalability. In several examples we show significantly improved focus on clinically important aspects in the rendered images.

Visualization systems for rendering images from a multi-dimensional data set, include an interactive visualization system configured to accept user input to define at least one explicit prioritized feature in an image rendered from a multi-dimensional image data set. The at least one prioritized feature is automatically electronically rendered with high or full quality in different interactively requested rendered images of the image data while other non-prioritized features are rendered at lower quality. The visualization system may optionally include a rendering system configured to render images by electronically assigning a level of detail for different tiles associated with an image, each level of detail having a number of pixel samples to be calculated to thereby accelerate image processing.

Volume raycasting techniques are important for both visual arts and visualization. They allow an efficient generation of visual effects and the visualization of scientific data obtained by tomography or numerical simulation. Thanks to their flexibility, experts agree that GPU-based raycasting is the state-of-the art technique for interactive volume rendering. It will most likely replace existing slice-based techniques in the near future. Volume rendering techniques are also effective for the direct rendering of implicit surfaces used for soft body animation and constructive solid geometry.

The lecture starts off with an in-depth introduction to the concepts behind GPU-based ray-casting to provide a common base for the following parts. The focus of this course is on advanced illumination techniques which approximate the physically-based light transport more convincingly. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion and simple Monte-Carlo based approaches to global illumination including translucency and scattering. With the proposed techniques, users are able to interactively create convincing images from volumetric data whose visual quality goes far beyond traditional approaches. The optical properties in participating media are defined using the phase function. Many approximations to the physically based light transport applied for rendering natural phenomena such as clouds or smoke assume a rather homogenous phase function model. For rendering volumetric scans on the other hand different phase function models are required to account for both surface-like structures and fuzzy boundaries in the data. Using volume rendering techniques, artists who create medical visualization for science magazines may now work on tomographic scans directly, without the necessity to fall back to creating polygonal models of anatomical structures.

Multiple-volume visualization is a growing field in medical imaging providing simultaneous exploration of volumes acquired from varying modalities. However, high complexity results in an increased strain on performance compared to single volume rendering as scenes may consist of volumes with arbitrary orientations and rendering is performed with varying sample densities. Expensive image order techniques such as depth peeling have previously been used to perform the necessary calculations. In. this work we present a view-independent region based scene description for multi-volume pipelines. Using Binary Space Partitioning we are able to create a simple interface providing all required information for advanced multi-volume renderings while introducing a minimal overhead for scenes with few volumes. The modularity of our solution is demonstrated by the use of visual development and performance is documented with benchmarks and real-time simulations.

Volume raycasting techniques are important for both visual arts and visualization. They allow an efficient generation of visual effects and the visualization of scientific data obtained by tomography or numerical simulation. Thanks to their flexibility, experts agree that GPU-based raycasting is the state-of-the art technique for interactive volume rendering. It will most likely replace existing slice-based techniques in the near future. Volume rendering techniques are also effective for the direct rendering of implicit surfaces used for soft body animation and constructive solid geometry.

The lecture starts off with an in-depth introduction to the concepts behind GPU-based ray-casting to provide a common base for the following parts. The focus of this course is on advanced illumination techniques which approximate the physically-based light transport more convincingly. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion and simple Monte-Carlo based approaches to global illumination including translucency and scattering. With the proposed techniques, users are able to interactively create convincing images from volumetric data whose visual quality goes far beyond traditional approaches. The optical properties in participating media are defined using the phase function. Many approximations to the physically based light transport applied for rendering natural phenomena such as clouds or smoke assume a rather homogenous phase function model. For rendering volumetric scans on the other hand different phase function models are required to account for both surface-like structures and fuzzy boundaries in the data. Using volume rendering techniques, artists who create medical visualization for science magazines may now work on tomographic scans directly, without the necessity to fall back to creating polygonal models of anatomical structures.

A novel technique for efficient computation of global light propagation in interactive DVR is presented in this paper. The approach is based on a combination of local shadows from the vicinity of each voxel with global shadows calculated at high resolution but stored in a sparser grid. The resulting intensities are then used as the initial illumination for an additional pass that computes first order scattering effects. The method captures global shadowing effects with enhanced shadows of near structures. A GPU framework is used to evaluate the illumination updates at interactive frame rates, using incremental refinements of the in-scattered light. 

We propose a framework which allows an application developer to construct and execute pipelines of existing CUDA kernel programs without programming the somewhat complex kernel configuration and setup. The framework is a new addition to the eXtensible Imaging Platform (XIP) of the National Cancer Institute. Pipeline construction is carried out through graphical construction of scene graphs in the XIP Builder tool. Complex pipeline struc-tures as well as kernels of arbitrary structure and function are supported. The framework has been used to execute existing CUDA kernels from NVIDIA’s CUDA SDK as well as a more complex image segmentation algorithm.

This paper introduces Temporal Density Parallel Coordinates (TDPC) and Depth Cue Parallel Coordinates (DCPC) which extend the standard 2D parallel coordinates technique to capture time-varying dynamics. The proposed techniques can be used to analyse temporal positions of data items as well as temporal positions of changes occurring using 2D displays. To represent temporal changes, polygons (instead of traditional lines) are rendered in parallel coordinates. The results presented show that rendering polygons is superior at revealing large temporal changes. Both TDPC and DCPC have been efficiently implemented on the GPU allowing the visualization of thousands of data items over thousands of time steps at interactive frame rates.

This paper introduces Temporal Density Parallel Coordinates (TDPC) and Depth Cue Parallel Coordinates (DCPC) which extend the standard 2D parallel coordinates technique to capture time-varying dynamics. The proposed techniques can be used to analyse temporal positions of data items as well as temporal positions of changes occurring using 2D displays. To represent temporal changes, polygons (instead of traditional lines) are rendered in parallel coordinates. The results presented show that rendering polygons is superior at revealing large temporal changes. Both TDPC and DCPC have been efficiently implemented on the GPU allowing the visualization of thousands of data items over thousands of time steps at interactive frame rates.

This paper introduces a novel technique to compute illumination for Direct Volume Rendering. By adding shadow effects to volume rendered images, the perception of shapes and tissue properties can be significantly improved and it has the potential to increase the diagnostic value of medical volume rendering. The integrated intensity of incident light for a voxel is computed using a local approximation of the ambient occlusion, thus avoiding the rendering of tissues with very low illumination. Luminous tissue effects are also introduced to enhance the illumination model, controlled through an emissive component in the transfer function. This effect allows the user to highlight specific structures and can give a better understanding of tissue density. Multiresolution volume management and GPU-based computation is used to significantly speed-up the calculations and to support large data sets. The scheme yields interactive frame rates for incrementally refined ambient and emissive illumination for arbitrary transfer function changes.

This paper presents state-of-the-art methods, which address the technical challenges in visualizing large three-dimensional (3D) data and enable rendering at interactive frame rates.

Direct volume rendering has proved to be an effective visualization method for medical data sets and has reached wide-spread clinical use. The diagnostic exploration, in essence, corresponds to a tissue classification task, which is often complex and time-consuming. Moreover, a major problem is the lack of information on the uncertainty of the classification, which can have dramatic consequences for the diagnosis. In this paper this problem is addressed by proposing animation methods to convey uncertainty in the rendering. The foundation is a probabilistic Transfer Function model which allows for direct user interaction with the classification. The rendering is animated by sampling the probability domain over time, which results in varying appearance for uncertain regions. A particularly promising application of this technique is a "sensitivity lens" applied to focus regions in the data set. The methods have been evaluated by radiologists in a study simulating the clinical task of stenosis assessment, in which the animation technique is shown to outperform traditional rendering in terms of assessment accuracy.

This paper presents a novel direct volume rendering technique for adaptive object- and image-space sampling density of multiresolution volumes. The raycasting is implemented entirely on the GPU in a single pass fragment program which adapts the sampling density along rays, guided by block resolutions. The multiresolution volumes are provided by a transfer function based level-of-detail scheme adaptively loading large out-of-core volumes. Adaptive image-space sampling is achieved by gathering projected basic volume block statistics for screen tiles and then allocating a level-of-detail for each tile. This combination of techniques provides a significant reduction of processing requirements while maintaining high quality rendering.

Direct Volume Rendering (DVR) is a technique for creating images directly from a representation of a function defined over a three-dimensional domain. The technique has many application fields, such as scientific visualization and medical imaging. A striking property of the data sets produced within these fields is their ever increasing size and complexity. Despite the advancements of computing resources these data sets seem to grow at even faster rates causing severe bottlenecks in terms of data transfer bandwidths, memory capacity and processing requirements in the rendering pipeline.

This thesis focuses on efficient methods for DVR of large data sets. At the core of the work lies a level-of-detail scheme that reduces the amount of data to process and handle, while optimizing the level-of-detail selection so that high visual quality is maintained. A set of techniques for domain knowledge encoding which significantly improves assessment and prediction of visual significance for blocks in a volume are introduced. A complete pipeline for DVR is presented that uses the data reduction achieved by the level-of-detail selection to minimize the data requirements in all stages. This leads to reduction of disk I/O as well as host and graphics memory. The data reduction is also exploited to improve the rendering performance in graphics hardware, employing adaptive sampling both within the volume and within the rendered image.

The developed techniques have been applied in particular to medical visualization of large data sets on commodity desktop computers using consumer graphics processors. The specific application of virtual autopsies has received much interest, and several developed data classification schemes and rendering techniques have been motivated by this application. The results are, however, general and applicable in many fields and significant performance and quality improvements over previous techniques are shown.

This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolution data from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added value and future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands of the procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsies involving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using levelof- detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large, out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of the pipeline are presented using data sets from forensic cases.

This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolutiondata from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added valueand future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands ofthe procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsiesinvolving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using levelof-detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large,out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of thepipeline are presented using data sets from forensic cases.

Direct Volume Rendering (DVR) is of increasing diagnostic value in the analysis of data sets captured using the latest medical imaging modalities. The deployment of DVR in everyday clinical work, however, has so far been limited. One contributing factor is that current Transfer Function (TF) models can encode only a small fraction of the user's domain knowledge. In this paper, we use histograms of local neighborhoods to capture tissue characteristics. This allows domain knowledge on spatial relations in the data set to be integrated into the TF. As a first example, we introduce Partial Range Histograms in an automatic tissue detection scheme and present its effectiveness in a clinical evaluation. We then use local histogram analysis to perform a classification where the tissue-type certainty is treated as a second TF dimension. The result is an enhanced rendering where tissues with overlapping intensity ranges can be discerned without requiring the user to explicitly define a complex, multidimensional TF.

Multi-dimensional Transfer Functions (MDTFs) are increasingly used in volume rendering to produce high quality visualizations of complex data sets. A major factor limiting the use of MDTFs is that the available design tools have not been simple enough to reach wide usage outside of the research context, for instance in clinical medical imaging. In this paper we address this problem by defining an MDTF design concept based on improved histogram display and interaction in an exploratory process. To this end we propose sorted histograms, 2D histograms that retain the intuitive appearance of a traditional 1D histogram while conveying a second attribute. We deploy the histograms in medical visualizations using data attributes capturing domain knowledge e.g. in terms of homogeneity and typical surrounding of tissues. The resulting renderings demonstrate that the proposed concept supports a vast number of visualization possibilities based on multi-dimensional attribute data.

We present a direct interblock interpolation technique that enables direct volume rendering of blocked, multiresolution volumes. The proposed method smoothly interpolates between blocks of arbitrary block-wise level-of-detail (LOD) without sample replication or padding. This permits extreme changes in resolution across block boundaries and removes the interblock dependency for the LOD creation process. In addition the full data reduction from the LOD selection can be maintained throughout the rendering pipeline. Our rendering pipeline employs a flat block subdivision followed by a transfer function based adaptive LOD scheme. We demonstrate the effectiveness of our method by rendering volumes of the order of gigabytes using consumer graphics cards on desktop PC systems.

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called 'feature animation' may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.

The high complexity of Transfer Function (TF) design is a major obstacle to widespread routine use of Direct Volume Rendering, particularly in the case of medical imaging. Both manual and automatic TF design schemes would benefit greatly from a fast and simple method for detection of tissue value ranges. To this end, we introduce the a-histogram, an enhancement that amplifies ranges corresponding to spatially coherent materials. The properties of the a-histogram have been explored for synthetic data sets and then successfully used to detect vessels in 20 Magnetic Resonance angiographies, proving the potential of this approach as a fast and simple technique for histogram enhancement in general and for TF construction in particular.

Direct Volume Rendering (DVR) is known to be of diagnostic value in the analysis of medical data sets. However, its deployment in everyday clinical use has so far been limited. Two major challenges are that the current methods for Transfer Function (TF) construction are too complex and that the tissue separation abilities of the TF need to be extended. In this paper we propose the use of histogram analysis in local neighborhoods to address both these conflicting problems. To reduce TF construction difficulty, we introduce Partial Range Histograms in an automatic tissue detection scheme, which in connection with Adaptive Trapezoids enable efficient TF design. To separate tissues with overlapping intensity ranges, we propose a fuzzy classification based on local histograms as a second TF dimension. This increases the power of the TF, while retaining intuitive presentation and interaction.

In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent tool for visualizing this type of data but has its limitations. This paper deals with one of its main limitations - how to visualize a large number of data items without hiding the inherent structure they constitute. We solve this problem by constructing clusters and using high precision textures to represent them. We also use transfer functions that operate on the high precision textures in order to highlight different aspects of the cluster characteristics. Providing predefined transfer functions as well as the support to draw customized transfer functions makes it possible to extract different aspects of the data. We also show how feature animation can be used as guidance when simultaneously analysing several clusters. This technique makes it possible to visually represent statistical information about clusters and thus guides the user, making the analysis process more efficient.

We propose an interactive visualization approach to finding a mathematical model for a real world process, commonly known in the field of control theory as system identification. The use of interactive visualization techniques provides the modeller with instant visual feedback which facilitates the model validation process. When working interactively with such large data sets, as are common in system identification, methods to handle this data efficiently are required. We are developing approaches based on data streaming to meet this need.

We propose an interactive visualization approach to finding a mathematical model for a real world process, commonly known in the field of control theory as system identification. The use of interactive visualization techniques provides the modeller with instant visual feedback which facilitates the model validation process. When working interactively with such large data sets, as are common in system identification, methods to handle this data efficiently are required. We are developing approaches based on data streaming to´meet this need.

The size of standard volumetric data sets in medical imaging is rapidly increasing causing severe performance limitations in direct volume rendering pipelines. The methods presented in this paper exploit the medical knowledge embedded in the transfer function to reduce the required bandwidth in the pipeline. Typically, medical transfer functions cause large subsets of the volume to give little or no contribution to the rendered image. Thus, parts of the volume can be represented at low resolution while retaining overall visual quality. This paper introduces the use of transfer functions at decompression time to guide a level-of-detail selection scheme. The method may be used in combination with traditional lossy or lossless compression schemes. We base our current implementation on a multi-resolution data representation using compressed wavelet transformed blocks. The presented results using the adaptive decompression demonstrate a significant reduction in the required amount of data while maintaining rendering quality. Even though the focus of this paper is medical imaging, the results are applicable to volume rendering in many other domains.

Regular and stochastic behavior in single particle orbits in static magnetic reversals have wide application in laboratory and physical plasmas. In a simple magnetic reversal, the system has three degrees of freedom but only two global (exact) constants of the motion, the system is nonintegrable and the particle motion can, under certain conditions, exhibit chaotic behavior. Here, we consider the dynamics when a constant shear field is added. In this case, the form of the potential changes from quadratic to velocity dependent. We use numerically integrated trajectories to show that the effect of the shear field is to break the symmetry of the system so that the topology of the invariant tori of regular orbits is changed. In this case, invariant tori take the form of nested Moebius strips in the presence of the shear field. The route to chaos is via bifurcation (period doubling) of the Moebius strip tori.

Regular and stochastic behavior in single particle orbits in static magnetic reversals have wide application in laboratory and physical plasmas. In a simple magnetic reversal, the system has three degrees of freedom but only two global (exact) constants of the motion; the system is nonintegrable and the particle motion can, under certain conditions, exhibit chaotic behavior. Here, we consider the dynamics when a constant shear field is added. In this case, the form of the potential changes from quadratic to velocity dependent.We use numerically integrated trajectories to showthat¨the effect of the shear field is to break the symmetry of the system so that the topology of the invariant tori of regular orbits is changed. In this case, invariant tori take the form of nested Moebius strips in the presence of the shear field. The route to chaos is via bifurcation (period doubling) of the Moebius strip tori.

We examine wave-particle interactions in a magnetized plasma. We present snapshots of an animation of the three-dimensional electron phase space distribution produced by an electrostatic wave propagating across a magnetic field. The distribution function has been evolved by a particle in cell simulation. The electron phase space has been visualized by distributing the simulation electrons over an array representing phase space density and by volume rendering this array. The results are, due to the choice of initial plasma and wave parameters, of relevance for electron acceleration at astrophysical shocks.