Illustrative context-preserving exploration of volume data

In volume rendering, it is very difficult to simultaneously visualize interior and exterior structures while preserving clear shape cues. Highly transparent transfer functions produce cluttered images with many overlapping structures, while clipping techniques completely remove possibly important context information. In this paper, we present a new model for volume rendering, inspired by techniques from illustration. It provides a means of interactively inspecting the interior of a volumetric data set in a feature-driven way which retains context information. The context-preserving volume rendering model uses a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. This new method represents an alternative to conventional clipping techniques, sharing their easy and intuitive user control, but does not suffer from the drawback of missing context information     

GPU-based point radiation for interactive volume sculpting and segmentation

Internal structures, features, and properties in volumetric datasets are mostly obscured and hidden. In order to reveal and explore them, appropriate tools are required to remove and carve the occluding materials and isolate and extract different regions of interest. We introduce a framework of interactive tools for real-time volume sculpting and segmentation. We utilize a GPU-based point radiation technique as a fundamental building block to create a collection of high-quality volume manipulation tools for direct drilling, lasering, peeling, and cutting/pasting. In addition, we enable interactive parallel region growing segmentation that allows multiple seed planting by direct sketching on different volumetric regions with segmentation results dynamically modified during the process. We use the same point radiation technique to create high-quality real-time feedback of the segmented regions during the seed growing process. We present results obtained from raw and segmented medical volume datasets.     

Interactive Visualization of Multimodal Volume Data for Neurosurgical Tumor Treatment

We present novel interactive methods for the visualization of multimodal volume data as used in neurosurgical therapy planning. These methods allow surgeons to explore multimodal volumes and focus on functional data and lesions. Computer graphics techniques are proposed to create expressive visualizations at interactive frame rates to reduce time‐consuming and complex interaction with the medical data. Contributions of our work are the distance‐based enhancements of functional data and lesions which allows the surgeon to perceive functional and anatomical structures at once and relate them directly to the intervention. In addition we propose methods for the visual exploration of the path to the structures of interest, to enhance anatomical landmarks, and to provide additional depth indicators. These techniques have been integrated in a visualization prototype that provides interaction capabilities for finding the optimal therapeutic strategy for the neurosurgeon.     

水下3维声场体可视化两种切平面实现方法

为了深入研究海洋水声环境对海军作战的影响,在水下声场体可视化的基础上对能量场进行3维剖分技术研究。根据不同的战术目的,探讨了两种不同的切平面剖分绘制方法。这两种方法相互补充,能够使指挥员直观准确地理解水下战场环境。     

Interactive clipping techniques for texture-based volume visualization and volume shading

We propose clipping methods that are capable of using complex geometries for volume clipping. The clipping tests exploit per-fragment operations on the graphics hardware to achieve high frame rates. In combination with texture-based volume rendering, these techniques enable the user to interactively select and explore regions of the data set. We present depth-based clipping techniques that analyze the depth structure of the boundary representation of the clip geometry to decide which parts of the volume have to be clipped. In another approach, a voxelized clip object is used to identify the clipped regions. Furthermore, the combination of volume clipping and volume shading is considered. An optical model is introduced to merge aspects of surface-based and volume-based illumination in order to achieve a consistent shading of the clipping surface. It is demonstrated how this model can be efficiently incorporated in the aforementioned clipping techniques.     

Progressive Expectation‐Maximization for Hierarchical Volumetric Photon Mapping

State‐of‐the‐art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel‐based approaches is the excessive number of photons that are required in practice to resolve fine illumination details, while controlling the amount of noise. In this paper, we propose a parametric density estimation technique that represents radiance using a hierarchical Gaussian mixture. We efficiently obtain the coefficients of this mixture using a progressive and accelerated form of the Expectation‐Maximization algorithm. After this step, we are able to create noise‐free renderings of high‐frequency illumination using only a few thousand Gaussian terms, where millions of photons are traditionally required. Temporal coherence is trivially supported within this framework, and the compact footprint is also useful in the context of real‐time visualization. We demonstrate a hierarchical ray tracing‐based implementation, as well as a fast splatting approach that can interactively render animated volume caustics.     

Hardware-Accelerated, High-Quality Rendering Based on Trivariate Splines Approximating Volume Data

We develop an approach for hardware‐accelerated, high‐quality rendering of volume data using trivariate splines. The proposed quasi‐interpolating schemes are realtime reconstructions. The low total degrees provide several advantages for our GPU implementation. In particular, intersecting rays with spline isosurfaces for direct Phong illumination is performed by simple root finding algorithms (analytic and iterative), while the necessary normals result from blossoming. Since visualizations are on a fragment base, our renderer for isosurfaces includes an automatic level of detail. While we use well‐known spatial data structures in the CPU part of the algorithm for hierarchical view frustum culling and memory reduction, our GPU implementations have to take the highly complex structure of the splines into account. These include an appropriate organization of the data streams, i.e. we develop an advanced encoding scheme for the spline coefficients, as well as an implicit scheme for bounding geometry retrieval. In addition, we propose an elaborated clipping procedure to be performed in the fragment shader. These features essentially reduce bus traffic, memory consumption, and data access on the GPU leading to interactive frame rates for renderings of high visual quality. Compared with pure CPU implementations and existing GPU implementations for trivariate polynomials frame rates increase by factors between 10 and 100.     

COPERNICUS: Context‐Preserving Engine for Route Navigation with Interactive User‐modifiable Scaling

In this paper, we present an automated system for generating context‐preserving route maps that depict navigation routes as a path between nodes and edges inside a topographic network. Our application identifies relevant context information to support navigation and orientation, and generates customizable route maps according to design principles that communicate all relevant context information clearly visible on one single page. Interactive scaling allows seamless transition between the original undistorted map and our new map design, and supports user‐specified scaling of regions of interest to create personalized driving directions according to the drivers needs.     

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.     

Scales and Scale‐like Structures

We present a method for generating scales and scale‐like structures on a polygonal mesh through surface replacement. As input, we require a triangular mesh that will be covered with scales and one or more proxy‐models to be used as the scale's shape. A user begins scale generation by drawing a lateral line on the model to control the distribution and orientation of scales on the surface. We then create a vector field over the surface to control an anisotropic Voronoi tessellation, which represents the region occupied by each scale. Next we replace these regions by cutting the proxy model to match the boundary of the Voronoi region and deform the cut model onto the surface. The result is a fully connected 2‐manifold that is suitable for subsequent post‐processing applications like surface subdivision.     

Intelligent GPGPU Classification in Volume Visualization: A framework based on Error‐Correcting Output Codes

In volume visualization, the definition of the regions of interest is inherently an iterative trial‐and‐error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi‐dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on‐demand multiple regions of interest. These methods can be split into a two‐level GPU‐based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error‐Correcting Output Codes (ECOC) framework. In a pre‐processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre‐labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi‐class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU‐OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy.     

LiveSync: deformed viewing spheres for knowledge-based navigation

Although real-time interactive volume rendering is available even for very large data sets, this visualization method is used quite rarely in the clinical practice. We suspect this is because it is very complicated and time consuming to adjust the parameters to achieve meaningful results. The clinician has to take care of the appropriate viewpoint, zooming, transfer function setup, clipping planes and other parameters. Because of this, most often only 2D slices of the data set are examined. Our work introduces LiveSync, a new concept to synchronize 2D slice views and volumetric views of medical data sets. Through intuitive picking actions on the slice, the users define the anatomical structures they are interested in. The 3D volumetric view is updated automatically with the goal that the users are provided with expressive result images. To achieve this live synchronization we use a minimal set of derived information without the need for segmented data sets or data-specific pre-computations. The components we consider are the picked point, slice view zoom, patient orientation, viewpoint history, local object shape and visibility. We introduce deformed viewing spheres which encode the viewpoint quality for the components. A combination of these deformed viewing spheres is used to estimate a good viewpoint. Our system provides the physician with synchronized views which help to gain deeper insight into the medical data with minimal user interaction.     

Thin Structures in Image Based Rendering

We propose a novel method to handle thin structures in Image‐Based Rendering (IBR), and specifically structures supported by simple geometric shapes such as planes, cylinders, etc. These structures, e.g. railings, fences, oven grills etc, are present in many man‐made environments and are extremely challenging for multi‐view 3D reconstruction, representing a major limitation of existing IBR methods. Our key insight is to exploit multi‐view information. After a handful of user clicks to specify the supporting geometry, we compute multi‐view and multi‐layer alpha mattes to extract the thin structures. We use two multi‐view terms in a graph‐cut segmentation, the first based on multi‐view foreground color prediction and the second ensuring multiview consistency of labels. Occlusion of the background can challenge reprojection error calculation and we use multiview median images and variance, with multiple layers of thin structures. Our end‐to‐end solution uses the multi‐layer segmentation to create per‐view mattes and the median colors and variance to create a clean background. We introduce a new multi‐pass IBR algorithm based on depth‐peeling to allow free‐viewpoint navigation of multi‐layer semi‐transparent thin structures. Our results show significant improvement in rendering quality for thin structures compared to previous image‐based rendering solutions.     

IStar: a raster representation for scalable image and volume data

Topology has been an important tool for analyzing scalar data and flow fields in visualization. In this work, we analyze the topology of multivariate image and volume data sets with discontinuities in order to create an efficient, raster-based representation we call IStar. Specifically, the topology information is used to create a dual structure that contains nodes and connectivity information for every segmentable region in the original data set. This graph structure, along with a sampled representation of the segmented data set, is embedded into a standard raster image which can then be substantially downsampled and compressed. During rendering, the raster image is upsampled and the dual graph is used to reconstruct the original function. Unlike traditional raster approaches, our representation can preserve sharp discontinuities at any level of magnification, much like scalable vector graphics. However, because our representation is raster-based, it is well suited to the real-time rendering pipeline. We demonstrate this by reconstructing our data sets on graphics hardware at real-time rates.     

基于改进卡方统计量的藏文文本表示方法

藏文文本表示是将非结构化的藏文文本转换为计算机能够处理的数据形式,是藏文文本分类、文本聚类等领域特征抽取的前提。传统的藏文文本表示方法较少考虑特征项之间的关联度,容易造成语义损失。为此,结合向量空间模型,提出一种新的藏文文本表示方法。提取文本中词频统计TF-IDF值较高的部分词项作为对比词项,对藏文文本进行断句处理,以每个句子作为一个语境主题,利用卡方统计量计算文本中词项与对比词项的关联程度。实验结果表明,与传统的向量空间模型相比,该方法能更准确地表示藏文文本。     

Output‐Sensitive Filtering of Streaming Volume Data

Real‐time volume data acquisition poses substantial challenges for the traditional visualization pipeline where data enhancement is typically seen as a pre‐processing step. In the case of 4D ultrasound data, for instance, costly processing operations to reduce noise and to remove artefacts need to be executed for every frame. To enable the use of high‐quality filtering operations in such scenarios, we propose an output‐sensitive approach to the visualization of streaming volume data. Our method evaluates the potential contribution of all voxels to the final image, allowing us to skip expensive processing operations that have little or no effect on the visualization. As filtering operations modify the data values which may affect the visibility, our main contribution is a fast scheme to predict their maximum effect on the final image. Our approach prioritizes filtering of voxels with high contribution to the final visualization based on a maximal permissible error per pixel. With zero permissible error, the optimized filtering will yield a result that is identical to filtering of the entire volume. We provide a thorough technical evaluation of the approach and demonstrate it on several typical scenarios that require on‐the‐fly processing.     

Fast Ray Sorting and Breadth‐First Packet Traversal for GPU Ray Tracing

We present a novel approach to ray tracing execution on commodity graphics hardware using CUDA. We decompose a standard ray tracing algorithm into several data‐parallel stages that are mapped efficiently to the massively parallel architecture of modern GPUs. These stages include: ray sorting into coherent packets, creation of frustums for packets, breadth‐first frustum traversal through a bounding volume hierarchy for the scene, and localized ray‐primitive intersections. We utilize the well known parallel primitives scan and segmented scan in order to process irregular data structures, to remove the need for a stack, and to minimize branch divergence in all stages. Our ray sorting stage is based on applying hash values to individual rays, ray stream compression, sorting and decompression. Our breadth‐first BVH traversal is based on parallel frustum‐bounding box intersection tests and parallel scan per each BVH level. We demonstrate our algorithm with area light sources to get a soft shadow effect and show that our concept is reasonable for GPU implementation. For the same data sets and ray‐primitive intersection routines our pipeline is ～3x faster than an optimized standard depth first ray tracing implemented in one kernel.     

Template-driven segmentation of confocal microscopy images

High quality 3D visualization of anatomic structures is necessary for many applications. The anatomic structures first need to be segmented. A variety of segmentation algorithms have been developed for this purpose. For confocal microscopy images, the noise introduced during the specimen preparation process, such as the procedure of penetration or staining, may cause images to be of low contrast in some regions. This property will make segmentation difficult. Also, the segmented structures may have rugged surfaces in 3D visualization. In this paper, we present a hybrid method that is suitable for segmentation of confocal microscopy images. A rough segmentation result is obtained from the atlas-based segmentation via affine registration. The boundaries of the segmentation result are close to the object boundaries, and are regarded as the initial contours of the active contour models. After convergence of the snake algorithm, the resulting contours in regions of low contrast are locally refined by parametric bicubic surfaces to alleviate the problem of incorrect convergence. The proposed method increases the accuracy of the snake algorithm because of better initial contours. Besides, it can provide smoother segmented results in 3D visualization.     

Visualizing High‐Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis

High‐dimensional data visualization is receiving increasing interest because of the growing abundance of high‐dimensional datasets. To understand such datasets, visualization of the structures present in the data, such as clusters, can be an invaluable tool. Structures may be present in the full high‐dimensional space, as well as in its subspaces. Two widely used methods to visualize high‐dimensional data are the scatter plot matrix (SPM) and the parallel coordinate plot (PCP). SPM allows a quick overview of the structures present in pairwise combinations of dimensions. On the other hand, PCP has the potential to visualize not only bi‐dimensional structures but also higher dimensional ones. A problem with SPM is that it suffers from crowding and clutter which makes interpretation hard. Approaches to reduce clutter are available in the literature, based on changing the order of the dimensions. However, usually this reordering has a high computational complexity. For effective visualization of high‐dimensional structures, also PCP requires a proper ordering of the dimensions. In this paper, we propose methods for reordering dimensions in PCP in such a way that high‐dimensional structures (if present) become easier to perceive. We also present a method for dimension reordering in SPM which yields results that are comparable to those of existing approaches, but at a much lower computational cost. Our approach is based on finding relevant subspaces for clustering using a quality criterion and cluster information. The quality computation and cluster detection are done in image space, using connected morphological operators. We demonstrate the potential of our approach for synthetic and astronomical datasets, and show that our method compares favorably with a number of existing approaches.     

Comparison of statistical models performance in case of?segmentation using a small amount of training datasets

Model-based image segmentation has been extensively used in medical imaging to learn both the shape and appearance of anatomical structures from training datasets. The more training datasets are used, the more accurate is the segmented model, as we account for more information about its variability. However, training datasets of large size with a proper sampling of the population may not always be available. In this paper, we compare the performance of statistical models in the context of lower limb bones segmentation using MR images when only a small number of datasets is available for training. For shape, both PCA-based priors and shape memory strategies are tested. For appearance, methods based on intensity profiles are tested, namely mean intensity profiles, multivariate Gaussian distributions of profiles and multimodal profiles from EM clustering. Segmentation results show that local and simple methods perform the best when a small number of datasets is available for training. Conversely, statistical methods feature the best segmentation results when the number of training datasets is increased.