Abstract Incorporating causal knowledge and mechanisms is essential for refining causal models and improving downstream tasks, such as designing new treatments. In this paper, we introduce a novel concept in causal discovery, termed interventional constraints , which differs fundamentally from interventional data. While interventional data require direct perturbations of variables, interventional constraints encode high-level causal knowledge in the form of inequality constraints on causal effects. For instance, in the Sachs dataset, Akt has been shown to be activated by PIP3, meaning PIP3 exerts a positive causal effect on Akt. Existing causal discovery methods allow enforcing structural constraints (e.g., requiring a causal path from PIP3 to Akt), but they may still produce incorrect causal conclusions, such as learning that “PIP3 inhibits Akt.” Interventional constraints bridge this gap by explicitly constraining the total causal effect between variable pairs, ensuring learned models respect known causal influences. To formalize interventional constraints, we adopt a metric to quantify total causal effects for linear causal models and formulate the problem as a constrained optimization task, solved using a two-stage constrained optimization method. We evaluate our approach on real-world datasets and demonstrate that integrating interventional constraints not only improves model accuracy and ensures consistency with established findings, making models more explainable, but also facilitates the discovery of new causal relationships that would otherwise be costly to identify.

Understanding the causal relationships between data variables can provide crucial insights into the construction of tabular datasets. Most existing causality learning methods typically focus on applying a single identifiable causal model, such as the Additive Noise Model (ANM) or the Linear non-Gaussian Acyclic Model (LiNGAM), to discover the dependencies exhibited in observational data. We improve on this approach by introducing a novel dual-step framework capable of performing both causal structure learning and tabular data synthesis under multiple causal model assumptions. Our approach uses Directed Acyclic Graphs (DAG) to represent causal relationships among data variables. By applying various functional causal models including ANM, LiNGAM and the Post-Nonlinear model (PNL), we implicitly learn the contents of DAG to simulate the generative process of observational data, effectively replicating the real data distribution. This is supported by a theoretical analysis to explain the multiple loss terms comprising the objective function of the framework. Experimental results demonstrate that DAGAF outperforms many existing methods in structure learning, achieving significantly lower Structural Hamming Distance (SHD) scores across both real-world and benchmark datasets (Sachs: 47%, Child: 11%, Hailfinder: 5%, Pathfinder: 7% improvement compared to state-of-the-art), while being able to produce diverse, high-quality samples.

As a predominant threat to women's health world-wide, breast cancer has become increasingly important in on-cology research. The discovery of molecular subtypes of breast cancer has led to more subtype oriented treatment and prognosis prediction. Effective prognosis models help to estimate the recurrence as well as the quality and duration of survival, leading to more personalized treatments. However, most traditional prognostic models either ignore molecular subtypes or only make limited use of them. The roles of molecular subtypes in the development and treatment of breast cancer have not been fully revealed. With the over 1200 cases collected by Sir Run Run Shaw Hospital of Zhejiang University in the past two decades, we aim to improve understanding of molecular subtypes and their impacts on the prognosis via data analysis in the long run. As the initial stage, this short paper presents our preliminary work of logistic regression experiments with the data. Though molecular subtypes have not been included the tentative model, they are to be explored further in following stages.

Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover “thematic topics” from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods.

Abstract Reminiscence is an important aspect in our life. It preserves precious memories, allows us to form our own identities and encourages us to accept the past. Our work takes the advantage of modern sensor technologies to support reminiscence, enabling self‐monitoring of personal activities and individual movement in space and time on a daily basis. This paper presents MyEvents, a web‐based personal visual analytics platform designed for non‐computing experts, that allows for the collection of long‐term location and movement data and the generation of event mementos. Our research is focused on two prominent goals in event reminiscence: (1) selection subjectivity and human involvement in the process of self‐knowledge discovery and memento creation; and (2) the enhancement of event familiarity by presenting target events and their related information for optimal memory recall and reminiscence. A novel multi‐significance event ranking model is proposed to determine significant events in the personal history according to user preferences for event category, frequency and regularity. The evaluation results show that MyEvents effectively fulfils the reminiscence goals and tasks.

Small to medium enterprises (SMEs), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck in the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPUprogramming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, the existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and are hard to use. Little attentions have been paid to the applicability, usability, and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system (GPSME) for inexperienced users to utilize the GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with a new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with nonexpert GPU users in four SMEs reflect that a GPSME system can efficiently accelerate real-world applications with at least 4× and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.

Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.

Cancer research has led to more cancer patients being cured, and many more enabled to live with their cancer. As such, some cancers are now considered a chronic disease, where patients and their families face the challenge to take an active role in their own care and in some cases in their treatment. To this direction the iManageCancer project aims to provide a cancer specific self-management platform designed according to the needs of patient groups while focusing, in parallel, on the wellbeing of the cancer patient. In this paper, we present the use-case requirements collected using a survey, a workshop and the analysis of three white papers and then we explain the corresponding system architecture. We describe in detail the main technological components of the designed platform, show the current status of development and we discuss further directions of research.

Effective management and visualization of scientific and research documents can greatly assist researchers by improving understanding of relationships (e.g. citations) between the documents. This paper presents work on the management and visualization of large corpuses of scientific papers in order to help researchers explore their citation relationships. Term selection and weighting are used for mining citation relationships by identifying the most relevant. To this end, we present a variation of the TF-IDF scheme, which uses external domain resources as references to calculate the term weighting in a particular domain; document weighting is taken into account in the calculation of term weighting from a group of citations. A simple hierarchical word weighting method is also presented. The work is supported by an underlying architecture for document management using NoSQL databases and employs a simple visualization interface.

Abstract Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state‐of‐the‐art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface‐extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many‐core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co‐operation within thread‐blocks and reducing redundant computation; ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10×) than state‐of‐the‐art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64×) on the same GPU.

Summary The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the computational complexity of the kernelized version of the algorithm grows quadratically with the number of training examples. To tackle this high computational complexity, we have developed a directive‐based approach that converts a gradient‐ascent based training algorithm for the CPU to an efficient graphics processing unit (GPU) implementation. We compare our GPU‐based SVM training algorithm to the standard LibSVM CPU implementation, a highly optimized GPU‐LibSVM implementation, as well as to a directive‐based OpenACC implementation. The results on different handwritten digit classification datasets demonstrate an important speed‐up for the current approach when compared to the CPU and OpenACC versions. Furthermore, our solution is almost as fast and sometimes even faster than the highly optimized CUBLAS‐based GPU‐LibSVM implementation, without sacrificing the algorithm's accuracy. Copyright © 2014 John Wiley & Sons, Ltd.

MyHealthAvatar is designed to provide a digital representation of patient health status. It aims to become a `lifetime companion' for individual citizens that will facilitate the collection of, and access to, long term health-status information. This avatar is not only extremely valuable for clinical decision-making, but it will generate data to support clinical investigation, thereby leading to strengthened multidisciplinary research and excellence in supporting innovative medical care across the population. My Health Avatar platform is currently under development. The purpose of this paper is to present the scope, the provided service and the future plans of the platform as well as a detailed description of the visual representation of the MyHealthAvatar platform.

The digital avatar is a vision for the digital representation of personal health status in body centric views. It is designed as an integrated facility that allows collection of, access to and sharing to life-long and consistent data. A number of Virtual Physiological Human (VPH) communities have started the movement to this direction by creating a digital patient road-map and by supporting data sharing infrastructures. As an innovative concept, the impact of digital patient and avatar to personalized medicine and treatment is yet to be clear. This requires a focused and concerted effort in addressing various questions regarding user perspective, use cases and scenarios. This paper presents use cases and future scenarios realizing the vision for the digital avatar as well as architectural consideration for the envisaged platform.

In this paper, we introduce a diagnosis and treatment for amblyopia performed through a game suitable for children aged between 3 and 7. Our method places emphasis on cooperation between the two eyes to achieve a good binocular outcome to aid the recovery of depth perception. Our approach is not limited to a particular device or platform nor even to a aprticular form of game. Several prototype games have been developed, including 2D games and 3D games.

The visualisation of whole-body anatomy has a variety of applications in health-related analysis and simulation. However, the rendering of complex 3D human anatomy models is generally performed by standalone applications rather than via a web interface, as rendering large 3D models has always been a weak spot of traditional web browsers. Consequently, online access to, and exploration of, the human anatomy in 3D has not been feasible in the past. With the advent of WebGL and HTML5, high performance OpenGL rendering seamlessly integrated with the web interface is now within reach, and this opens the possibility of visualising avatar-centered health data via a web interface. In this paper, a WebGL-based prototype for rendering whole-body anatomy is introduced, and the technical details are presented.

This paper presents the proof-of-concept design of the data repository for 4D digital avatars in the MyHealthAvatar project. Taking account of the privacy and legal issues of patient health information, the research generates a set of synthetic data based on publicly available survey data. At the prototype stage, these synthetic data are used in the scenarios of data storage and management. The paper discusses the early proof-of-concept design of the technical stack which enables the storage and query of large scale patients' health data and empowers the future data mining and analysis for health care support. It provides the first stage implementation and the use of it for data analytics.

Volume up scaling enlarges the size of a volume to make feature analysis more accurate and efficient. Linear interpolation, widely used in volume up scaling, result in jagged artifacts around features and losses of high-frequency components. Based on the example-based up scaling framework, this paper presents a new high-quality volume up scaling technique, predicting the high-frequency components by searching for the best matched patch in the input volume. As each slice can be taken as an image, the existing image up scaling technique based on the local self-similarity assumption can be directly applied to achieve slice up scaling. We further validate that the local self-similarity assumption is still valid for 3D volumes, and we extend this technique to 3D volume up scaling, i.e., isotropic volume up scaling. We compare our volume up scaling technique with traditional linear and cubic spline interpolations, and demonstrate that our method can generate a higher quality volume with better shape and details preserved. The proposed volume up scaling technique is well suitable for legacy low-resolution volumes to improve their visual qualities in visualization and analysis.

In many biomedical applications, it is often desired to simulate, analyse and visualise the dynamics of a particular patient based on a patient-specific musculoskeletal model. However, reconstructing a patient-specific model directly from medical images is highly labour intensive, and impractical in the clinical context. A more efficient method is to derive it from an atlas musculoskeletal model using patient-specific hints. In this paper, Laplacian mesh processing is introduced to deform an atlas model to a patient-specific model, based on patient-specific landmarks extracted from two orthogonal clinical images and using least-squares error optimization. Muscle attachment landmarks and motion landmarks in the atlas are also transformed as part of the process. Drift and inter-surface penetrations are prevented by supplementary inter-surface landmarks. Mesh simplification and reconstruction are used to avoid out-of-memory failures that may result from trying to deform models at high resolution.

Abstract The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called and . only solves for k by marginalizing the image prior, while recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyses the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported failure. Then we show that the leading methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. , although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size. To overcome these limitations, we introduce an inter‐scale prior prediction scheme and a principled mechanism for integrating the sharpening filter into . Both qualitative results and extensive quantitative comparisons demonstrate that our algorithm outperforms state‐of‐the‐art methods.

Blind motion deblurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. Although significant progress has been made on tackling this problem, existing methods, when applied to highly diverse natural images, are still far from stable. This paper focuses on the robustness of blind motion deblurring methods toward image diversity-a critical problem that has been previously neglected for years. We classify the existing methods into two schemes and analyze their robustness using an image set consisting of 1.2 million natural images. The first scheme is edge-specific, as it relies on the detection and prediction of large-scale step edges. This scheme is sensitive to the diversity of the image edges in natural images. The second scheme is nonedge-specific and explores various image statistics, such as the prior distributions. This scheme is sensitive to statistical variation over different images. Based on the analysis, we address the robustness by proposing a novel nonedge-specific adaptive scheme (NEAS), which features a new prior that is adaptive to the variety of textures in natural images. By comparing the performance of NEAS against the existing methods on a very large image set, we demonstrate its advance beyond the state-of-the-art.

We present a novel approach for GPU-based high-quality volume rendering of large out-of-core volume data. By focusing on the locations and costs of ray traversal, we are able to significantly reduce the rendering time over traditional algorithms. We store a volume in an octree (of bricks); in addition, every brick is further split into regular macrocells. Our solutions move the branch-intensive accelerating structure traversal out of the GPU raycasting loop and introduce an efficient empty-space culling method by rasterizing the proxy geometry of a view-dependent cut of the octree nodes. This rasterization pass can capture all of the bricks that the ray penetrates in a per-pixel list. Since the per-pixel list is captured in a front-to-back order, our raycasting pass needs only to cast rays inside the tighter ray segments. As a result, we achieve two levels of empty space skipping: the brick level and the macrocell level. During evaluation and testing, this technique achieved 2 to 4 times faster rendering speed than a current state-of-the-art algorithm across a variety of data sets.

Lighting design is a complex, but fundamental, problem in many fields. In volume visualization, direct volume rendering generates an informative image without external lighting, as each voxel itself emits radiance. However, external lighting further improves the shape and detail perception of features, and it also determines the effectiveness of the communication of feature information. The human visual system is highly effective in extracting structural information from images, and to assist it further, this paper presents an approach to structure-aware automatic lighting design by measuring the structural changes between the images with and without external lighting. Given a transfer function and a viewpoint, the optimal lighting parameters are those that provide the greatest enhancement to structural information - the shape and detail information of features are conveyed most clearly by the optimal lighting parameters. Besides lighting goodness, the proposed metric can also be used to evaluate lighting similarity and stability between two sets of lighting parameters. Lighting similarity can be used to optimize the selection of multiple light sources so that different light sources can reveal distinct structural information. Our experiments with several volume data sets demonstrate the effectiveness of the structure-aware lighting design approach. It is well suited to use by novices as it requires little technical understanding of the rendering parameters associated with direct volume rendering.

Halos are usually used to enhance depth perception and display spatial relationships in illustrative visualization. In this paper, we present a simple and effective method to create volumetric halo illustration. In the pre-processing stage, we generate a view-independent halo intensity volume, which contains all potential halos around the boundaries of features, based on the opacity volume on graphic hardware. During halo rendering, the halo intensity volume is used to extract halos only around the contours of structures for the current viewpoint. The performance of our approach is significantly faster than previous halo illustration methods, which perform both halo generation and rendering during volume rendering. Experimental results demonstrate volumetric halo effects and the efficiency of the proposed approach.

Simulators of tumour growth can estimate the evolution of tumour volume and the quantity of various categories of cells as functions of time. However, the execution time of each simulation often takes several dozens of minutes (depending upon the dataset resolution), which clearly prevents easy interaction. The modern graphics processing unit (GPU) is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic performance and memory bandwidth that substantially outpaces its CPU counterpart. However, despite this, the GPU is little used in the context of the Virtual Physiological Human (VPH). This paper provides a case study to demonstrate the performance advantages that can be gained by using the GPU appropriately in the context of a VPH project in which the study of tumour growth is a central activity. We also analyse the algorithm performance on different modern parallel processing architectures, including multicore CPU and many-core GPU.

Medical visualisation is an indispensable means for doctors and researchers to better explore and analyse medical images. The EC-funded ContraCancrum project, which aims at more predictable tumour simulation and treatment, uses visualisation as an important tool to interactively display tumour development and tumour simulation. This paper presents CCVis - the visualisation tool in ContraCancrum - which is a Qt-based plug in of the DrEye platform. CCVis uses a unified architecture for visualisation of the patient image data and tumour simulation data. It provides axis-aligned and arbitrary slice views, is surface rendering and volume ray casting as well as time-varying visualisation of patient image series and simulation data. Statistics in tumour simulation are plotted as 2D graphs. Major extensions of the Visualization Toolkit (VTK) are made to meet the demands for label highlighting and multi-dimensional transfer function.

While there has been significant progress in the treatment of ischemic heart failure, it remains a significant health and economic problem worldwide. In this paper, we present the challenges of modelling ischemic heart failure and introduce a user-friendly software system that will be a sub-set of the Virtual Pathological Heart environment which is currently being developed under the FP7 VPH2 project. This will provide patient-specific computational modelling and simulation of the human heart to assist the cardiologist and the cardiac surgeon in defining the severity and extent of disease in patients with post-ischemic Left Ventricular Dysfunction. The proposed system will provide visualisation tools for surgical assessment and planning: the registration and display of necrotic and hypo-kinetic regions; simulated surgical restoration (cutting and patching); and finally post-operative functional prediction (volume, shape and mitral valve regurgitation).

Web services provide remote access to distributed resources and processes through uniform interfaces. However, the latency associated with data transmission has meant that they are generally applied to non-interactive data processing. Interactive applications, in which many more user interactions and data transmissions are involved, are difficult to adapt to web service based frameworks, particularly if the interactive investigation involves large datasets. In medical imaging and visualisation, user interactions are generally a prerequisite for the detailed study and manipulation of data. As a result of major scientific initiatives, such as the Virtual Physiological Human, in which large data repositories are being set up at a variety of sites, it is becoming increasingly common for the data being investigated to be stored on a remote server. Consequently, it is now highly desirable to develop a means by which web service based interactive visualisation can be applied to distributed medical data access and clinical collaboration. This paper presents a functional-level plug-in based architecture for interactive data visualisation via web services which is being implemented within the EC-funded ContraCancrum project.

Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this paper, we propose a new approach to tissue segmentation of 3D brain MRI using semi-supervised spectral clustering. Spectral clustering algorithm is more powerful than traditional clustering algorithms since it models the voxel-to-voxel relationship as opposed to voxel-to-cluster relationships. In the semi-supervised spectral clustering, two types of instance-level constraints: must-link and cannot-link as background prior knowledge are incorporated into spectral clustering, and the self-tuning parameter is applied to avoid the selection of the scaling parameter of spectral clustering. The semi-supervised spectral clustering is an effective tissue segmentation method because of its advantages in (1) better discovery of real data structure since there is no cluster shape restriction, (2) high quality segmentation results as it can obtain the global optimal solutions in the relaxed continuous domain by eigen-decomposition and combines the pairwise constraints information. Experimental results on simulated and real MRI data demonstrate its effectiveness.

Abstract Empty‐space skipping is an essential acceleration technique for volume rendering. Image‐order empty‐space skipping is not well suited to GPU implementation, since it must perform checks on, essentially, a per‐sample basis, as in kd‐tree traversal, which can lead to a great deal of divergent branching at runtime, which is very expensive in a modern GPU pipeline. In contrast, object‐order empty‐space skipping is extremely fast on a GPU and has negligible overheads compared with approaches without empty‐space skipping, since it employs the hardware unit for rasterisation. However, previous object‐order algorithms have been able to skip only exterior empty space and not the interior empty space that lies inside or between volume objects. In this paper, we address these issues by proposing a multi‐layer depth‐peeling approach that can obtain all of the depth layers of the tight‐fitting bounding geometry of the isosurface by a single rasterising pass. The maximum count of layers peeled by our approach can be up to thousands, while maintaining 32‐bit float‐point accuracy, which was not possible previously. By raytracing only the valid ray segments between each consecutive pair of depth layers, we can skip both the interior and exterior empty space efficiently. In comparisons with 3 state‐of‐the‐art GPU isosurface rendering algorithms, this technique achieved much faster rendering across a variety of data sets.

Viewpoint selection is becoming a useful part in the volume visualization pipeline, as it further improves the efficiency of data understanding by providing representative viewpoints. We present two structure-aware view descriptors, which are the shape view descriptor and the detail view descriptor, to select the optimal viewpoint with the maximum amount of the structural information. These two proposed structure-aware view descriptors are both based on the gradient direction, as the gradient is a well-defined measurement of boundary structures, which have been proved as features of interest in many applications. The shape view descriptor is designed to evaluate the overall orientation of features of interest. For estimating local details, we employ the bilateral filter to construct the shape volume. The bilateral filter is very effective in smoothing local details and preserving strong boundary structures at the same time. Therefore, large-scale global structures are in the shape volume, while small-scale local details still remain in the original volume. The detail view descriptor measures the amount of visible details on boundary structures in terms of variances in the local structure between the shape volume and the original volume. These two view descriptors can be integrated into a viewpoint selection framework, and this framework can emphasize global structures or local details with flexibility tailored to the user's specific situations. We performed experiments on various types of volume datasets. These experiments verify the effectiveness of our proposed view descriptors, and the proposed viewpoint selection framework actually locates the optimal viewpoints that show the maximum amount of the structural information.

Abstract In this paper, we propose an efficient solution that addresses the performance problems of current single‐pass GPU raycasting algorithms. Our paper provides more control over the rendering process by introducing tighter ray segments for raycasting, while at the same time avoiding the introduction of any new rendering artefacts. We achieve this by dynamically generating, on the GPU, a coarsely fitted proxy geometry, composed of spheres, for the active blocks. The spheres are then rasterised into two z‐buffers by a single rendering pass. The resulting two z‐buffers are used as the first‐hit and last‐hit points for the subsequent raycaster. With this approach, only the valid ray segments between the two z‐buffers need to be sampled during raycasting. This also provides more coherent parallelism on the GPU due to more consistent ray length and avoidance of the overheads and dynamic branching of performing checks on a per‐sample basis during the raycasting pass. Our technique is ideal for dynamic data exploration in which both the transfer function and view parameters need to be changed frequently at runtime. The rendering results of our algorithm are identical to the general cube‐based proxy geometry algorithm, but the performance can be up to 15.7 times faster. Furthermore, the approach can be adopted by any existing raycasting system in a straightforward way.

Abstract This paper presents a fast, high‐quality, GPU‐based isosurface rendering pipeline for implicit surfaces defined by a regular volumetric grid. GPUs are designed primarily for use with polygonal primitives, rather than volume primitives, but here we directly treat each volume cell as a single rendering primitive by designing a vertex program and fragment program on a commodity GPU. Compared with previous raycasting methods, ours has a more effective memory footprint (cache locality) and better coherence between multiple parallel SIMD processors. Furthermore, we extend and speed up our approach by introducing a new view‐dependent sorting algorithm to take advantage of the early‐z‐culling feature of the GPU to gain significant performance speed‐up. As another advantage, this sorting algorithm makes multiple transparent isosurfaces rendering available almost for free. Finally, we demonstrate the effectiveness and quality of our techniques in several real‐time rendering scenarios and include analysis and comparisons with previous work.

Abstract This paper presents a novel surface texture synthesis method, which is capable of producing high‐quality results by performing the synthesis within an effective multi‐resolution scheme using an adaptive texture similarity metric. Compared with related works, our method allows us to directly carry out multi‐resolution synthesis without involving complicated operations such as mesh hierarchy and partitioning on the target surface. Also, the adaptive similarity metric focuses on measuring texture properties at different scales ranging from local to global, allowing for consistency within differently‐sized texture structures. Further, with the introduced round shaped neighbourhoods, we can save considerable amount of computation for the surface texture synthesis over variable texture directions. Experimental results are provided and comparisons are made against other latest works.

Abstract This paper proposes a patch‐based texture synthesis approach that cuts and stitches irregularly shaped texture patches to generate new texture images with minimized visual discontinuity. It works well on a wide range of textures. A semiautomatic algorithm is developed to obtain the irregularly shaped patches. To synthesize strictly structured textures, a regular pasting method is proposed to identify the texture structures and subsequently position the irregularly shaped patches according to the identified structures. The results and comparisons with related work are given.

We give a brief overview of a new source of data on human anatomy and kinematics created by the EC-funded project VAKHUM. The motivation was to provide support for education at various levels through animated 3D models and finite element meshes of human joints, while making original high-quality raw data available for industrial users. We outline some identified user demands. We further describe the data types and relevant software components employed for accessing and visualising VAKHUM models. The emphasis is on a novel user interface based on focal points and intuitive 3D controls for viewing anatomy that is appropriately registered with kinematics captured by local e.g. electrogoniometry or global e.g. stereophotogrammetry means. The operating scenario for accessing models in the VAKHUM database is outlined. We close with a summary of the work and pointers to future development.

Muscle simulation is an important component of human modeling, but there have been few attempts to demonstrate, in 3D and in an anatomically correct way, the structures of muscles and the way in which these change during motion. This paper proposes an anatomically-based approach to muscle modeling that attempts to provide models for human musculature based on the real morphological structures. These models provide a good visual description of muscle form and action and represent a sound base from which to produce further progress toward medically accurate simulation of human bodies. Three major problems have been addressed: geometric modeling, deformation and texture. To allow for the wide variety of deformable muscle shapes encountered in the body, while retaining as many of their common properties as possible, the geometric models are classified into several categories according to the characteristics of their structures and actions. Within each category, the model for each muscle has an efficient structural form, created using anatomical data. Deformation is also performed on the basis of the categories, with all models within each category sharing the same deformation scheme. The categories cover both general and special cases. The result is an efficient, anatomically accurate muscle representation that is specifically designed to accommodate the particular form of deformation exhibited by each individual muscle. Interactions between muscles; are also taken into account to avoid penetration occurring between adjacent muscles in our model. To provide a suitable visual effect, the muscle texture is generated directly on the model surface. The textures and colors are obtained from anatomical data via image analysis. Some results are presented on the geometric modeling, the deformation and the texture of muscles related to the lower limb.

Navigation path planning is a crucial part of virtual endoscopy, but the identification of the optimal fly-through path is a time-intensive process. This paper describes a prototype system for virtual endoscopy and proposes a slice-based approach to calculating the path of the virtual camera that uses the slice borderlines of region-of-interest images to obtain the approximate centreline, centralised locally with respect to the hollow object wall. By using the 2D slice borderlines to express the hollow object boundary, the path searching and polygon intersection computing are simplified and made more efficient. Finally, we present results of our algorithm tested on the Visible Human data set.

Presents a method concerning the volume rendering of fine details, such as blood vessels and nerves, from medical data. The realistic and efficient visualization of such structures is often of great medical interest, and conventional rendering techniques do not always deal with them adequately. Our method uses preprocessing to reconstruct fine details that are difficult to segment and label. It detects the presence of fine geometrical structures, such as cracks or cylinders that suggest the existence of, for example, blood vessels or nerves; the subsequent volume rendering then displays fine geometrical objects that lie on a surface. The method can also show structures within the volume, using a special "integration sampling" scheme to portray reconstructed volume texture, such as that exhibited by muscle fibers. By combining the surface structure and volume texture in the rendering, realistic results can be produced; examples are provided.

Muscle simulation should be an important component of human modelling, but to date there have been few attempts to demonstrate muscle structures in an anatomically-correct way. This paper proposes a feature-based approach to muscle modelling which attempts to provide models for human musculature based on the real anatomical structures. These models provide a good visual description and form a sound basis for further developments towards medically-accurate simulation of human bodies. To allow for the wide variety of muscle shapes encountered in the body, the geometric models are based on muscle features identified from radiological data. These results are realistic models with correct anatomical structures.

Large‐sized volume datasets have recently become commonplace and users are now demanding that volume‐rendering techniques to visualise such data provide acceptable results on relatively modest computing platforms. The widespread use of the Internet for the transmission and/or rendering of volume data is also exerting increasing demands on software providers. Multiresolution can address these issues in an elegant way. One of the fastest volume‐rendering alrogithms is that proposed by Lacroute & Levoy 1 , which is based on shear‐warp factorisation and min‐max octrees (MMOs). Unfortunately, since an MMO captures only a single resolution of a volume dataset, this method is unsuitable for rendering datasets in a multiresolution form. This paper adapts the above algorithm to multiresolution volume rendering to enable near‐real‐time interaction to take place on a standard PC. It also permits the user to modify classification functions and/or resolution during rendering with no significant loss of rendering speed. A newly‐developed data structure based on the MMO is employed, the multiresolution min‐max octree, M 3 O, which captures the spatial coherence for datasets at all resolutions. Speed is enhanced by the use of multiresolution opacity transfer functions for rapidly determining and discarding transparent dataset regions. Some experimental results on sample volume datasets are presented.

The steady growth of the Internet has dramatically changed the way information is shared and modern users expect near real time delivery, high quality images together with in-depth navigation and exploration of 3D models. Multiresolution is a promising approach for fast distributed volume visualisation employing levels-of-detail. We review multiresolution algorithms and visualisation systems on the WWW. Our discussion is based on experience gained in the development of the IAEVA-II project funded by the European Commission. A new method for rapid data classification/rendering of multiresolution volumes based on shear-warp factorisation, is described. We can change classification functions and data resolution during rendering without significant reduction in interactivity. A method for constructing multiresolution transfer functions for determining opacity is also investigated. Finally, future trends in developing WWW visualisation systems are discussed.