利用Stein-Weiss解析函数性质的3维血管分割

目的 针对现有的血管分割方法对血管的分割精度尚有不足,尤其是对噪声等影响下的断裂血管,基于Stein-Weiss函数的解析性提出了一种新的3维血管分割算法,能够分割出更精细更清晰的血管。方法 首先,通过图像增强和窗宽窗位调节的预处理来增加血管点与背景的对比度。然后,将Stein-Weiss函数与梯度算子结合起来,把CT体数据的每一个体素都表示为一个Stein-Weiss函数,体素6邻域的灰度值作为Stein-Weiss函数各组成部分的系数。再求出Stein-Weiss函数在x、y、z 3个方向上的梯度值,大于某一个阈值时,便将此体素视为血管边缘上的点。最后,根据提取出血管边缘的2维CT切片重建出3维的血管。结果 对肝静脉的造影数据S70进行肝脏血管分割与3维重建的实验结果表明,利用该算法进行血管分割的敏感性和特异性相对于区域生长算法和八元数解析分割算法都较高。尤其是对于血管分割的去噪方面有明显优势,因此能够快速有效地分割出更清晰更精细的血管。结论 提出了一种新的血管分割算法,利用Stein-Weiss函数的解析性来提取血管的边缘,实验结果表明,此算法可以有效快速地去除血管噪声并得到更精细的分割结果。由于Stein-Weiss解析的性质可以适合任意维数,所以利用Stein-Weiss解析函数性质可以进行2维或更高维的图像边缘识别。     

Analysis of time-dependent flow-sensitive PC-MRI data

Many flow visualization techniques, especially integration-based methods, are problematic when the measured data exhibit noise and discretization issues. Particularly, this is the case for flow-sensitive phase-contrast magnetic resonance imaging (PC-MRI) data sets which not only record anatomic information, but also time-varying flow information. We propose a novel approach for the visualization of such data sets using integration-based methods. Our ideas are based upon finite-time Lyapunov exponents (FTLE) and enable identification of vessel boundaries in the data as high regions of separation. This allows us to correctly restrict integration-based visualization to blood vessels. We validate our technique by comparing our approach to existing anatomy-based methods as well as addressing the benefits and limitations of using FTLE to restrict flow. We also discuss the importance of parameters, i.e., advection length and data resolution, in establishing a well-defined vessel boundary. We extract appropriate flow lines and surfaces that enable the visualization of blood flow within the vessels. We further enhance the visualization by analyzing flow behavior in the seeded region and generating simplified depictions.     

Real-time illustration of vascular structures

We present real-time vascular visualization methods, which extend on illustrative rendering techniques to particularly accentuate spatial depth and to improve the perceptive separation of important vascular properties such as branching level and supply area. The resulting visualization can and has already been used for direct projection on a patient's organ in the operation theater where the varying absorption and reflection characteristics of the surface limit the use of color. The important contributions of our work are a GPU-based hatching algorithm for complex tubular structures that emphasizes shape and depth as well as GPU-accelerated shadow-like depth indicators, which enable reliable comparisons of depth distances in a static monoscopic 3D visualization. In addition, we verify the expressiveness of our illustration methods in a large, quantitative study with 160 subjects.     

A Survey of Perceptually Motivated 3D Visualization of Medical Image Data

This survey provides an overview of perceptually motivated techniques for the visualization of medical image data, including physics‐based lighting techniques as well as illustrative rendering that incorporate spatial depth and shape cues. Additionally, we discuss evaluations that were conducted in order to study the perceptual effects of these visualization techniques as compared to conventional techniques. These evaluations assessed depth and shape perception with depth judgment, orientation matching, and related tasks. This overview of existing techniques and their evaluation serves as a basis for defining the evaluation process of medical visualizations and to discuss a research agenda.     

慢阻肺患者CT图像中肺内血管分割及定量分析

目的 肺内血管形态结构的改变是慢性阻塞性肺疾病（慢阻肺）的一种重要病理改变。针对慢阻肺中肺血管疾病的定量评估问题,提出一种基于各向异性连续最大流的肺内血管自动分割方法,并定量分析不同半径的肺内血管体积分布,以研究慢阻肺病程中肺血管重塑规律。方法 使用U-Net分割肺体,获取肺脏区域,减少后续血管增强与分割的运算量;借助基于多尺度Hessian矩阵的血管增强方法,获得血管的似然增强结果和轴向方向;将血管似然结果和轴向信息以数据保真项和各向异性正则项的形式融入到连续最大流分割框架,实现肺血管的自动分割。结果 在公开数据集ArteryVein和仿真数据集VascuSynth上对肺内血管分割方法的有效性进行测试;在从4家医院收集的614例临床影像数据上分析小半径血管体积占比情况,对比慢阻肺组与非慢阻肺组之间肺血管重塑差异。肺血管分割方面,对于增加不同程度的高斯噪声（σ=5,10,15,20,25,30,35）的VascuSynth仿真数据,本文方法获得的Dice值分别为0.87,0.80,0.77,0.75,0.73,0.71,0.69;对于低剂量数据集ArteryVein,Dice值为0.79。肺血管定量分析方面,非慢阻肺组和慢阻肺组的小血管体积平均占比值为0.656±0.067,0.589±0.074。不同慢阻肺分级GOLD1—4组小血管占比为0.612±0.051、0.600±0.078、0.565±0.067、0.528±0.053。结论 本文提出的肺内血管算法可以用于肺血管重塑研究,通过实验分析验证了非慢阻肺组与慢阻肺组小血管体积占比存在显著差异;基于慢阻肺分级指数（global initiative for chronic obstructive pulmonary disease,GOLD）的不同慢阻肺病人之间,小血管体积占比在轻症和重症之间也存在显著差异。     

Particle-based non-photorealistic volume visualization

Non-photorealistic techniques are usually applied to produce stylistic renderings. In visualization, these techniques are often able to simplify data, producing clearer images than traditional visualization methods. We investigate the use of particle systems for visualizing volume datasets using non-photorealistic techniques. In our VolumeFlies framework, user-selectable rules affect particles to produce a variety of illustrative styles in a unified way. The techniques presented do not require the generation of explicit intermediary surfaces.     

定向区域生长算法及其在血管分割中的应用

针对医学图像中微细管道结构灰度连续性差,采用常规区域生长法进行分割容易丢失末梢的问题,提出一种定向区域生长算法,可以在生长过程中跨越管道结构中的低灰度 区域。算法向图像中已生长区域外灰度最高的方向进行生长,每次将一个体素加入已生长区域,将图像转变为一颗以种子点为根结点的树,再从叶子结点进行回溯以确定感兴趣区 域。对实现算法的数据结构进行了讨论。算法可以应用于任意维的图像。对2维和3维图像的测试结果表明,相对于常规的区域生长法,算法可以分割出更多的血管分支。算法对3维 图像的运行时间为秒钟量级,可以满足临床应用的要求。     

A flexible approach for visual data mining

The exploration of heterogenous information spaces requires suitable mining methods as well as effective visual interfaces. Most of the existing systems concentrate either on mining algorithms or on visualization techniques. This paper describes a flexible framework for visual data mining which combines analytical and visual methods to achieve a better understanding of the information space. We provide several pre-processing methods for unstructured information spaces, such as a flexible hierarchy generation with user-controlled refinement. Moreover, we develop new visualization techniques, including an intuitive focus+context technique to visualize complex hierarchical graphs. A special feature of our system is a new paradigm for visualizing information structures within their frame of reference     

Software visualization in the large

The invisible nature of software hides system complexity, particularly for large team-oriented projects. The authors have evolved four innovative visual representations of code to help solve this problem: line representation; pixel representation; file summary representation; and hierarchical representation. We first describe these four visual code representations and then discuss the interaction techniques for manipulating them. We illustrate our software visualization techniques through five case studies. The first three focus on software history and static software characteristics; the last two discuss execution behavior. The software library and its implementation are then described. Finally, we briefly review some related work and compare and contrast our different techniques for visualizing software     

基于GPU的血管造影图像增强方法*

血管增强扩散算法遵循多尺度方法,利用非线性各向异性扩散方法进行血管增强,该方法在可视化不同半径的血管和增强血管外观上比现存的大部分方法都要好,但医学图像数据分辨率和灰度级都很高,多尺度选择和求解非线性各向异性扩散的偏微分方程时运算量很大,执行速率低,不适合实际应用。提出一种基于GPU(graphic processing unit)的血管造影图像增强方法,采用计算统一设备架构(CUDA)技术,利用像素的独立性和偏微分方程求解的并发性,实现了并行血管增强扩散算法。实验结果表明,该方法在保持血管增强效果一样的同时降低了处理时间,加速比达到27倍以上。     

Visualizing Group Structures in Graphs: A Survey

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group‐only, group–node, group–edge and group–network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.     

Designing pixel-oriented visualization techniques: theory andapplications

Visualization techniques are of increasing importance in exploring and analyzing large amounts of multidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class of pixel-oriented techniques. The basic idea of pixel-oriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. A number of different pixel-oriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number of issues which are important in developing pixel-oriented visualization techniques. The major goal of this article is to provide a formal basis of pixel-oriented visualization techniques and show that the design decisions in developing them can be seen as solutions of well-defined optimization problems. This is true for the mapping of the data values to colors, the arrangement of pixels inside the subwindows, the shape of the subwindows, and the ordering of the dimension subwindows. The paper also discusses the design issues of special variants of pixel-oriented techniques for visualizing large spatial data sets     

Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features

Diabetic retinopathy (DR) is the major ophthalmic pathological cause for loss of eye sight due to changes in blood vessel structure. The retinal blood vessel morphology helps to identify the successive stages of a number of sight threatening diseases and thereby paves a way to classify its severity. This paper presents an automated retinal vessel segmentation technique using neural network, which can be used in computer analysis of retinal images, e.g., in automated screening for diabetic retinopathy. Furthermore, the algorithm proposed in this paper can be used for the analysis of vascular structures of the human retina. Changes in retinal vasculature are one of the main symptoms of diseases like hypertension and diabetes mellitus. Since the size of typical retinal vessel is only a few pixels wide, it is critical to obtain precise measurements of vascular width using automated retinal image analysis. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels are identified by means of a multilayer perceptron neural network, for which the inputs are derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network is utilized in our method. The performance of our technique is evaluated and tested on publicly available DRIVE database and we have obtained illustrative vessel segmentation results for those images.     

Survey of glyph-based visualization techniques for spatial multivariate medical data

In this survey article, we review glyph-based visualization techniques that have been exploited when visualizing spatial multivariate medical data. To classify these techniques, we derive a taxonomy of glyph properties that is based on classification concepts established in information visualization. Considering both the glyph visualization as well as the interaction techniques that are employed to generate or explore the glyph visualization, we are able to classify glyph techniques into two main groups: those supporting pre-attentive and those supporting attentive processing. With respect to this classification, we review glyph-based techniques described in the medical visualization literature. Based on the outcome of the literature review, we propose design guidelines for glyph visualizations in the medical domain.     

Adaptive Surface Visualization of Vessels with Animated Blood Flow

The investigation of hemodynamic information for the assessment of cardiovascular diseases (CVDs) gained importance in recent years. Improved flow measuring modalities and computational fluid dynamics (CFD) simulations yield in reliable blood flow information. For a visual exploration of the flow information, domain experts are used to investigate the flow information combined with its enclosed vessel anatomy. Since the flow is spatially embedded in the surrounding vessel surface, occlusion problems have to be resolved. A visual reduction of the vessel surface that still provides important anatomical features is required. We accomplish this by applying an adaptive surface visualization inspired by the suggestive contour measure. Furthermore, an illustration is employed to highlight the animated pathlines and to emphasize nearby surface regions. Our approach combines several visualization techniques to improve the perception of surface shape and depth. Thereby, we ensure appropriate visibility of the embedded flow information, which can be depicted with established or advanced flow visualization techniques. We apply our approach to cerebral aneurysms and aortas with simulated and measured blood flow. An informal user feedback with nine domain experts, we confirm the advantages of our approach compared with existing methods, e.g. semi‐transparent surface rendering. Additionally, we assessed the applicability and usefulness of the pathline animation with highlighting nearby surface regions.     

Visualizing big network traffic data using frequent pattern mining and hypergraphs

Visualizing communication logs, like NetFlow records, is extremely useful for numerous tasks that need to analyze network traffic traces, like network planning, performance monitoring, and troubleshooting. Communication logs, however, can be massive, which necessitates designing effective visualization techniques for large data sets. To address this problem, we introduce a novel network traffic visualization scheme based on the key ideas of (1) exploiting frequent itemset mining (FIM) to visualize a succinct set of interesting traffic patterns extracted from large traces of communication logs; and (2) visualizing extracted patterns as hypergraphs that clearly display multi-attribute associations. We demonstrate case studies that support the utility of our visualization scheme and show that it enables the visualization of substantially larger data sets than existing network traffic visualization schemes based on parallel-coordinate plots or graphs. For example, we show that our scheme can easily visualize the patterns of more than 41 million NetFlow records. Previous research has explored using parallel-coordinate plots for visualizing network traffic flows. However, such plots do not scale to data sets with thousands of even millions of flows.     

Visual computing for medical diagnosis and treatment

Diagnostic algorithms and efficient visualization techniques are of major importance for preoperative decisions, intra-operative imaging and image-guided surgery. Complex diagnostic decisions are characterized by a high information flow and fast decisions, requiring efficient and intuitive presentation of complex medical data and precision in the visualization. For intra-operative medical treatment, the pre-operative visualization results of the diagnostic systems have to be transferred to the patient on the operation room table. Via augmented reality, additional information of the hidden regions can be displayed virtually. This state-of-the-art report summarizes visual computing algorithms for medical diagnosis and treatment. After starting with direct volume rendering and tagged volume rendering as general techniques for visualizing anatomical structures, we go into more detail by focusing on the visualization of tissue and vessel structures. Afterwards, algorithms and techniques that are used for medical treatment in the context of image-guided surgery, intra-operative imaging and augmented reality are discussed and reviewed.     

Feature-preserving volume data reduction and focus+context visualization

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

Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy

Diabetic retinopathy screening involves assessment of the retina with attention to a series of indicative features, i.e., blood vessels, optic disk and macula etc. The detection of changes in blood vessel structure and flow due to either vessel narrowing, complete occlusions or neovascularization is of great importance. Blood vessel segmentation is the basic foundation while developing retinal screening systems since vessels serve as one of the main retinal landmark features. This article presents an automated method for enhancement and segmentation of blood vessels in retinal images. We present a method that uses 2-D Gabor wavelet for vessel enhancement due to their ability to enhance directional structures and a new multilayered thresholding technique for accurate vessel segmentation. The strength of proposed segmentation technique is that it performs well for large variations in illumination and even for capturing the thinnest vessels. The system is tested on publicly available retinal images databases of manually labeled images, i.e., DRIVE and STARE. The proposed method for blood vessel segmentation achieves an average accuracy of 94.85% and an average area under the receiver operating characteristic curve of 0.9669. We compare our method with recently published methods and experimental results show that proposed method gives better results.     

A Survey on Visualizations for Musical Data

Digital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non-expert users in data analysis and in gaining new knowledge. This survey focuses on this unique link between musicology and visualization. We classify 129 related works according to the visualized data types, and we analyse which visualization techniques were applied for certain research inquiries and to fulfill specific tasks. Next to scientific references, we take commercial music software and public websites into account, that contribute novel concepts of visualizing musicological data. We encounter different aspects of uncertainty as major problems when dealing with musicological data and show how occurring inconsistencies are processed and visually communicated. Drawing from our overview in the field, we identify open challenges for research on the interface of musicology and visualization to be tackled in the future.