频域体绘制中的颜色合成

和传统体绘制算法相比，频域体绘制算法具有较低的算法复杂度，能够更快地完成对三维数据场的显示．然而这种方法却不能简捷有效地表示和处理数据场的深度信息，因此其生成的图象缺少深度的遮挡效果．有鉴于此，Ｌｅｖｏｙ等曾通过诸如线性深度补偿的技术来弥补这一缺憾．尽管如此，运用这种技术得到的结果和传统空域方法仍有明显的差别．本文首先着眼于对空域体绘制方法中广泛采用的离散颜色合成技术的分析，然后引出了透明度密度分布函数的概念，进而推导出连续形式颜色合成过程的解析描述．根据这个结果，只要对三维数据场的透明度分布加以适当限制，就能够通过频域体绘制方法来实现颜色合成，增加绘制的深度效果．作者最后指出，基本频域体绘制方法和Ｌｅｖｏｙ的线性深度补偿方法的实质可以看成是这种颜色合成过程的近似．文中对透明度均匀分布的情况做了详细讨论，并且给出了具体算法，同时用生成的图象实例相应地进行了比较．     

局部特征加强的体绘制算法

最大强度差值累积结合了直接体绘制和最大强度值投影的优势,但其在累积过程中会遗漏一些局部特征.为了绘制体数据中局部特征信息,提出一种局部特征加强的直接体绘制方法.通过查找采样光线上特征边界点来确定局部最大强度的区域,利用局部差值累积的方法加强特征区域的绘制;为了提高特征分界点的查找精度,引入移动最小二乘法来平滑采样光线上的标量值,并利用用户自定义的阈值函数来控制特征的绘制;在绘制过程中,采用特征分析的表面光照模型增强绘制特征的三维立体感,引入深度信息对局部特征累积算法进行优化,并引入了Tone衰减方法使累积颜色值处于正常显示范围.实验结果表明,文中方法可在不需要传输函数的前提下绘制体数据中的特征信息.     

面向医学数据的分层剥离体绘制算法

采用直接体绘制方法显示医学数据时,一般通过修改传递函数来显示感兴趣区域,但仅通过调节传递函数很难清晰地显示被遮挡的人体组织,为此提出一种新的分层剥离直接体绘制算法.通过计算相邻的不透明度极小值和极大值之间的斜率,并根据用户指定的斜率阈值和累积不透明度阈值确定分层点,实现对体数据的分层绘制.实验结果证明,该算法可以根据不同医学数据的特征信息更精确地定位分层点,绘制效果清晰,速度较快.     

一种快速的体数据特征增强可视化算法

传输函数是实现体数据特征分类与可视化的重要手段,随着维度的升高,传输函数设计过程趋于复杂且难以实时交互;而在基于视线方向上特征分析的体数据特征可视化方法中,复杂的特征分析过程占用GPU硬件资源,降低了体绘制效率.为此提出一种快速的、不完全依赖于传输函数设计的体数据特征增强可视化算法.利用视线方向上采样点标量值变化率,快速识别视线方向上的重要特征,通过降低不重要特征的不透明度以增强重要特征在绘制结果图像中的可见性;引入反锐化掩模技术对视线方向上重要特征进行加权处理,在保证体绘制效率的同时增强了体数据特征的视觉感知.在绘制过程中,文中算法利用简单而有效的特征分析操作,在传输函数映射的基础上对感兴趣特征的视觉元素再次优化,实现不完全依赖于复杂传输函数设计的体数据特征增强可视化.最后通过大量实验,进一步验证了该算法的有效性与实用性.     

基于有效裁剪被遮挡单元的非规则数据场体绘制算法

提出了一种被遮挡单元的裁剪算法,以加速非规则数据场的体绘制过程。在基于一组平行切割平面的体绘制方法上,新算法通过对图像不透明度缓冲区中的值进行求平均操作,并将所计算的结果存储在一个与不透明度缓冲区间同样大小的平均不透明度缓冲区中,使得只需根据每一数据单元重心投影点在平均不透明度缓冲区中的值,就可得到此数据单元的可见性,从而有效裁剪掉被遮挡单元,降低需处理的数据量,加速体绘制过程。     

利用有序数据结构实现Shear-Warp加速算法

提出一种有序数据结构,在不影响算法效果的前提下,改进并加速Shear-Warp算法的运行速度.将体数据每个层片编码成以体素值为序的有序数组,依据不透明度函数确定不透明体素所对应的体素值范围.通过对有序数组的截取,快速定位不透明体素,跳过所有的透明体素,提高绘制速度.在普通配置计算机上验证该算法,绘制过程一般在数秒内即可完成.该算法思路明晰、操作便捷,在不影响图像质量的前提下显著提高绘制速度,满足实时性的要求.     

一种改进的基于CUDA的纹理映射和光线投射结合的体绘制算法

针对传统的基于GPU的光线投射算法绘制效率较低的问题,利用CUDA架构的并行计算特性和对三维纹理的处理能力进行改进和优化.将体数据映射为三维纹理,利用CUDA三维数组进行存储与绑定,纹理拾取的浮点返回值利用线性滤波进行平滑.在传输函数的设计中引入中心差分梯度幅值增强对体数据边界面的绘制效果.每条光线的求交及颜色积累采用并行计算,按照由前向后进行颜色及不透明度累积.设置不透明度阈值,采用不透明度提前终止加速绘制.实验结果表明,绘制速度较传统的基于GPU算法有10％的速度提升,绘制效果也有很大的改善.     

基于结构特征的自适应光线投射算法

目的 光线投射法是一种重要的直接体绘制算法,但其效果取决于复杂的传递函数.为此提出基于结构特征的自适应光线投射算法,从而使得利用简单传递函数即可很好地揭示体数据特征.方法 首先分析光线方向标量值的变化趋势获取结构特征——特征段;然后基于若干意义明确启发式规则(特征段的次序、尺度、重要度)自动计算特征段的可见度,根据特征段可见度调节每个采样点的不透明度;最后基于调节后的不透明度完成绘制.结果 使用合成数据、医学真实采样数据和工业CT(computed tomography)数据进行测试,结果表明本文算法在展示体数据的内部结构特征,尤其是细小结构方面优于其他类似算法;本文算法速度比DVR(direct volume rendering)慢,但仍可满足交互需求.另外,本文算法还提供多个形象直观、意义明确的参数供用户调节,进一步增加了本文算法的灵活性.结论 本文提出的自适应光线投射算法,允许用户使用简单传递函数和调节意义明显的参数即可有效揭示体数据特征,进一步提高了光线透射法的直观性.     

应用混合蛙跳算法的体绘制最佳视点选择

提出一种基于混合蛙跳算法的体绘制最佳视点选择方法。利用体数据投影图像的不透明度和亮度以及提取的结构信息特征,建立反映体素重要性和体数据内部结构信息的视点评价函数;将视点评价函数作为混合蛙跳算法的适应度函数,用混合蛙跳算法来指导和优化体绘制最佳视点的选择过程,以得到全局最优视点或一组被优化的视点集。实验表明,该方法能够快速有效地聚焦和显示体数据中的重要结构信息或感兴趣区域,算法的收敛速度和收敛精度高,具有良好的全局最佳视点选择性能,能够用来指导大规模体数据场的体绘制过程。     

特征抽象的直接体绘制方法

直接体绘制需要借助于传输函数,而设计一个有效的传输函数非常耗时且需要具备丰富的经验.为此提出一种不透明度自动调节的可视化方法.通过分析采样光线提取出数据的特征,并将这些特征抽象为不同层次的采样点,抽象采样点的不透明度根据采样光线上特征数的变化而改变;在保证最远抽象采样点可见度最大的前提下,推导并修改传统体绘制积分方程,得到基于抽象采样点的体绘制积分方程.实验结果表明,该方法不依赖于传输函数,能有效地展示体数据中的特征信息.     

Opacity volume based halo generation and depth-dependent halos

Halos are generally 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. At the preprocessing stage, we generate, on graphics hardware, a view-independent halo intensity volume, which contains all of the potential halos around the boundaries of features, based on the opacity volume. 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 direct volume rendering. We further propose depth-dependent halo effects, including depth color fading and depth width fading. These halo effects adaptively modulate the visual properties of halos to provide more perceptual cues for depth interpretation. Experimental results demonstrate the efficiency of our proposed approach and the effectiveness of depth-dependent halos.     

Tissue classification based on 3D local intensity structures forvolume rendering

This paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values     

模拟单元渲染的网络实时再现

研究以模拟单元渲染的方法表示灯具在立方体空间6个面上的照明效果.在进行模拟单元渲染时,对每个面进行方格划分,并按线性插值得到方格中心点,以每个方格中心点处的光强值代替整个方格的光强值.为了消除遮挡面,采用了深度序列算法,使渲染效果清楚、直观.整个渲染的过程在Flash平台上实现,满足更多用户的选择使用.     

Data Intermixing and Multi-volume Rendering

The main difference between multi-volume rendering and mono-volume rendering is data intermixing. In this paper, we present three levels of data intermixing and their rendering pipelines in direct multi-volume rendering, which discriminate image level intensity intermixing, accumulation level opacity intermixing, and illumination model level parameter intermixing. In the context of radiotherapy treatment planning, different data intermixing methods are applied to three volumes, including CT volume, Dose volume, and Segmentation volume, to compare the features of different data intermixing methods.     

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     

彩色三维体数据场的直接体绘制方法

提出基于光线投射和三维纹理映射的彩色体数据成像算法，研究如何从每个体素的R，G，B三元组映射成不透明度值，即不透明度转换函数。首先把原始的RGB色彩空间转换成LUV色彩空间；然后以亮度分量的中心差分来近似估计法向量，并应用Phong光照模型进行着色，根据亮度分量及其梯度等信息计算不透明度值；最后合成、累积颜色。对美国数字人男子照相彩色体数据分别采用两种算法进行实验。结果表明：基于光线投射的彩色体数据算法成像质量较高，可以表现体表毛细血管等细微结构，但速度较慢；基于三维纹理映射的彩色体数据成像算法速度较快，但成像质量适中。     

一种基于交互式多维传递函数的纹理映射体绘制算法

本文设计了一种基于空间信息的交互式多维传递函数的纹理映射体绘制算法。该算法不仅可以根据体数据的强度而且还利用体素的空间位置来设定绘制的颜色和阻光度。通过采用一种独特的空间投影变换,根据用户需求,将体数据划分为不同区域,并分别定义各自的传递函数。该特点使得本文的算法可以有效地对体数据进行交互式分析。在算法实现中,利用了通用图形硬件的可编程特性,在普通PC上可以达到理想的绘制质量和交互速度。     

Efficient opacity specification based on feature visibilities in direct volume rendering

Due to 3D occlusion, the specification of proper opacities in direct volume rendering is a time‐consuming and unintuitive process. The visibility histograms introduced by Correa and Ma reflect the effect of occlusion by measuring the influence of each sample in the histogram to the rendered image. However, the visibility is defined on individual samples, while volume exploration focuses on conveying the spatial relationships between features. Moreover, the high computational cost and large memory requirement limits its application in multi‐dimensional transfer function design. In this paper, we extend visibility histograms to feature visibility, which measures the contribution of each feature in the rendered image. Compared to visibility histograms, it has two distinctive advantages for opacity specification. First, the user can directly specify the visibilities for features and the opacities are automatically generated using an optimization algorithm. Second, its calculation requires only one rendering pass with no additional memory requirement. This feature visibility based opacity specification is fast and compatible with all types of transfer function design. Furthermore, we introduce a two‐step volume exploration scheme, in which an automatic optimization is first performed to provide a clear illustration of the spatial relationship and then the user adjusts the visibilities directly to achieve the desired feature enhancement. The effectiveness of this scheme is demonstrated by experimental results on several volumetric datasets.