Interactive visualization of volumetric white matter connectivity in DT-MRI using a parallel-hardware Hamilton-Jacobi solver

In this paper we present a method to compute and visualize volumetric white matter connectivity in diffusion tensor magnetic resonance imaging (DT-MRI) using a Hamilton-Jacobi (H-J) solver on the GPU (Graphics Processing Unit). Paths through the volume are assigned costs that are lower if they are consistent with the preferred diffusion directions. The proposed method finds a set of voxels in the DTI volume that contain paths between two regions whose costs are within a threshold of the optimal path. The result is a volumetric optimal path analysis, which is driven by clinical and scientific questions relating to the connectivity between various known anatomical regions of the brain. To solve the minimal path problem quickly, we introduce a novel numerical algorithm for solving H-J equations, which we call the Fast Iterative Method (FIM). This algorithm is well-adapted to parallel architectures, and we present a GPU-based implementation, which runs roughly 50-100 times faster than traditional CPU-based solvers for anisotropic H-J equations. The proposed system allows users to freely change the endpoints of interesting pathways and to visualize the optimal volumetric path between them at an interactive rate. We demonstrate the proposed method on some synthetic and real DT-MRI datasets and compare the performance with existing methods.     

全局脑白质纤维群智能跟踪算法

为了解决概率纤维跟踪算法"过度"误跟踪,效率低的缺点,受蚁群路径搜索过程群体协作模式启发,提出一种全局脑白质纤维群体智能跟踪方法。首先,构建了一种全局纤维度量指标,综合考虑局部纤维方向分布和全局纤维走向,并利用贝叶斯方法建立局部纤维方向分布不确定信息模型。其次,提出一种群智能全局优化算法。该算法构建基于von Miser-fisher分布函数的信息素模型,通过信息素模型诱导迭代优化纤维轨迹。人工合成数据实验结果表明,跟概率跟踪算法相比,该算法解决了纤维局部误差积累导致的误跟踪问题,相对误差降低至原来的二分之一,计算规模降低至原来的十分之一。实际临床数据验证了算法的有效性。     

一种去除机载LiDAR航带重叠区冗余点云的方法

机载LiDAR系统在获取高密度地表点云的同时,也带来了数据冗余的问题,特别是在航带重叠区中尤为突出。旨在研究无完整航迹信息辅助下去除航带重叠点,提出了基于点云GPS时间直方图的去除航带重叠点的方法。该方法包括三个步骤:(1)建立点云的GPS时间直方图,并根据GPS时间直方图特点获取航带重叠区外包矩形以及外包矩形中的所有点云;(2)考虑到城市中高密度点云有助于建筑物的三维重建,通过滤波分类处理获取建筑物点并予以全部保留;(3)对重叠区中除建筑物点外的其他所有点进行格网数据组织并根据GPS时间直方图逐格网去除航带冗余点。实验结果表明,该方法能较好地保留建筑物点的同时高效去除航带重叠点且不依赖于航迹信息,提高了后续数据分析处理的效率。     

3D可视化输电线路中杆塔空间挂点计算

将3D可视化技术应用于输电线路管理是当前电力信息化的发展方向.地球是一个不规则椭球体,很难计算出杆塔挂点在地球空间的坐标,因此无法真实、准确地表达线路与杆塔挂点的连接情况.为此,结合大量分析和研究,提出了一种基于Google Earth的杆塔空间挂点计算方法,并应用于所开发的输电线路管理系统中.实践表明,该方法具有计算简单、空间定位精度高、线路与挂点连接逼真等特点,能够满足现有应用的要求.     

逼近点云的三角形网格的边缘误差减小研究

基于SOFM神经网络构建的三角形网格模型可以实现测量点云压缩后的Delaunay三角逼近剖分,但该模型存在边缘误差。为减小三角形网格的边缘误差,改进了三角形网格模型的训练模式,提出了3步训练模式。第1步采用整个测量点云,对三角形网格模型中的所有神经元进行整体训练;第2步采用测量点云中的边界点集,对三角形网格模型中的网格边界神经元进行训练;第3步采用边界点集中的角点点集,对与边界角点匹配最佳的网格边界神经元进行训练。算例表明,应用该训练模式,可以有效减小三角形网格的边缘误差,三角形网格逼近散乱点云的逼近精度得到提高并覆盖散乱点云整体分布范围。     

基于混沌的微弱三角波信号检测方法

利用混沌系统的相轨迹对初值敏感而列噪声免疫的特性,提出采用三角波和正弦波分别作策动力对微弱三角波信号的幅值进行检测。最后,在Matlab／Simulink环境下进行仿真实验。结论表明：混沌理论可以实现对信噪比很低的三角波信号幅值进行检测,并且取得较高的精度。     

分割多发性硬化症白质病灶的新方法

多发性硬化症(MS)是一种慢性的中枢神经系统疾病,其病灶可由常规脑部核磁共振成像(cMRI)进行检测。为提高图像处理的效率,提出了一种自动分割cMRI图像中的MS白质病灶(WML)的新方法。首先将模糊核聚类（KFCM)用于预处理后的T1加权像,得到白质图像;然后利用一个种子点的区域生长处理白质图像,提取出一个二值模板。该模板与对应的T2加权像进行乘积,得到一幅仅包含白质、病灶及背景的图像;最后再次利用KFCM分割图像,得到病灶的核心部分。实验结果表明,所提出的方法能快速、有效地分割出低噪声仿真图像中的WML,且Dice相似性系数平均值在80%以上。     

非线性三角恋模型及其在高斯白噪声激励下的基本动力学特征

研究了非线性三角恋模型在某些特定参数下的一些基本动力学特征,包括对称性、耗散性、不动点（平衡）,混沌行为和混沌吸引子.由于心理系统具有高度的复杂性和不确定性,本文用标准高斯白噪声刻画随机因素,构建了随机激励下的爱情模型,并研究了新模型的随机动力学行为,得到了不同激励幅值下系统的响应结果.     

由点云数据重建的隐式曲面的可视化算法

针对由点云数据重建的隐式曲面提出一种新的基于粒子系统的可视化算法。首先,基于平行线束的初始化方法在隐式模型表面找到均匀分布的采样点,避免原来粒子系统中的分割-死亡过程;用共轭梯度法替代原来粒子系统中的梯度下降法作为优化算法,将每一个椭圆粒子累进移动到低能量状态,避免了较长的收敛时间和围绕最小值的摆动现象;用贪婪选择法选择能够覆盖整个曲面的且不产生空洞的活动子集;松弛过程进一步改善依赖曲率的各向异性粒子采样。本文的粒子专门为基于Splats的表示法而设计,可以直接转换为椭圆Splats而不需要任何改动。因此,本算法可以快速、高质量地绘制出复杂隐式曲面模型。     

Does the difference between information and scientific visualization really matter?

Is it necessary to continue to define a difference between information and scientific visualization? Is the determined need for these differences creating confusion rather than helping investigators understand how to effectively apply visual display techniques to data and information? The author considers how cartographic and geographic information techniques seem to span both scientific and information visualization. She discusses the future directions in bioinformatics visualization.     

Graphical interface for motion editing using procedure visualization

In this paper, we introduce an interface for motion editing that visualizes editing procedures. For intuitive understanding and construction of motion editing procedures, our system represents an editing element that produces modified motion from a given input motion as a graphical node, and enables user to construct a whole motion editing procedure by connecting the nodes with a few mouse interactions. The system provides both the 3D transform manipulator and the time-line slider for intuitive controlling ...     

基于外点法的混合遗传算法求解约束优化问题

提出了一种求解约束优化问题的混合遗传算法。它不是传统的在适应值函数中加一个惩罚项，而是在初始种群、交叉运算和变异运算过程中，把违反约束条件的个体用外点法处理设计出新的实数编码遗传算法。数值实验证明，新算法性能优于现有其他进化算法，是通用性强、高效稳健的方法。该方法兼顾了遗传算法和外点法的优点，既有较快的收敛速度，又能以非常大的概率求得约束优化问题全局最优解。     

A new approach for data clustering and visualization using self-organizing maps

A self-organizing map (SOM) is a nonlinear, unsupervised neural network model that could be used for applications of data clustering and visualization. One of the major shortcomings of the SOM algorithm is the difficulty for non-expert users to interpret the information involved in a trained SOM. In this paper, this problem is tackled by introducing an enhanced version of the proposed visualization method which consists of three major steps: (1) calculating single-linkage inter-neuron distance, (2) calculating the number of data points in each neuron, and (3) finding cluster boundary. The experimental results show that the proposed approach has the strong ability to demonstrate the data distribution, inter-neuron distances, and cluster boundary, effectively. The experimental results indicate that the effects of visualization of the proposed algorithm are better than that of other visualization methods. Furthermore, our proposed visualization scheme is not only intuitively easy understanding of the clustering results, but also having good visualization effects on unlabeled data sets.     

Curvature estimation scheme for triangle meshes using biquadratic Bézier patches

When dealing with triangle meshes, it is often important to compute curvature information for the purposes of feature recognition, segmentation, or shape analysis. Since a triangle mesh is a piecewise linear surface, curvature has to be estimated. Several different schemes have been proposed, both discrete and continuous, i.e. based on fitting surfaces locally. This paper compares commonly used discrete and continuous curvature estimation schemes. We also present a novel method which uses biquadratic Bézier patches as a local surface fitting technique.     

3D Graph cut with new edge weights for cerebral white matter segmentation

Accurate and efficient automatic or semi-automatic brain image segmentation methods are of great interest to both scientific and clinical researchers of the human central neural system. Cerebral white matter segmentation in brain Magnetic Resonance Imaging (MRI) data becomes a challenging problem due to a combination of several factors like low contrast, presence of noise and imaging artifacts, partial volume effects, intrinsic tissue variation due to neurodevelopment and neuropathologies, and the highly convoluted geometry of the cortex. In this paper, we propose a new set of edge weights for the traditional graph cut algorithm (Boykov and Jolly, 2001) to correctly segment the cerebral white matter from T1-weighted MRI sequence. In this algorithm, the edge weights of Boykov and Jolly (2001) are modified by comparing the probabilities of an individual voxel and its neighboring voxels to belong to different segmentation classes. A shape prior in form of a series of ellipses is used next to model the contours of the human skull in various 2D slices in the sequence. This shape constraint is imposed to prune the original graph constructed from the input to form a subgraph consisting of voxels within the skull contours. Our graph cut algorithm with new set of edge weights is applied to the above subgraph, thereby increasing the segmentation accuracy as well as decreasing the computation time. Average segmentation errors for the proposed algorithm, the graph cut algorithm (Boykov and Jolly, 2001), and the Expectation Maximization Segmentation (EMS) algorithm Van Leemput et al., 2001 in terms of Dice coefficients are found to be (3.72 ± 1.12)%, (14.88 ± 1.69)%, and (11.95 ± 5.2)%, respectively.     

Computer-assisted categorization of brain computerized tomography pixels into cerebrospinal fluid, white matter, and gray matter

A computer-assisted method was employed to estimate the amounts of cerebrospinal fluid (CSF), white matter, and gray matter in individual computerized tomography (CT) scans of brains. By means of an image processing procedure (DMORPH), the means +/- SD CT numbers of "pure" CSF, white matter, and gray matter were determined in each scan and stored. A CATSEG program used these means to define ranges for CT numbers for each of the three tissues on each scan, and to assign each pixel in a scan to one of the three categories. Summing over seven serial scans provided volumetric estimates of CSF, white matter, and gray matter in a brain segment. For 10 subjects aged 21 to 43 years, CSF volume equaled 1.4 to 4.7% of the total segment volume, white matter equaled 37.5 to 48.2%, and gray matter equaled 50.2 to 58.9%. Image processing hardware and software which allow standardized sampling from CT images for the evaluation of surface areas and CT numbers are described. These procedures, as applied to CT scans of the human brain, can be used to estimate the volumes of CSF, white matter, and gray matter in a selected intracranial segment.     

Categorical data visualization and clustering using subjective factors

Clustering is an important data mining problem. However, most earlier work on clustering focused on numeric attributes which have a natural ordering to their attribute values. Recently, clustering data with categorical attributes, whose attribute values do not have a natural ordering, has received more attention. A common issue in cluster analysis is that there is no single correct answer to the number of clusters, since cluster analysis involves human subjective judgement. Interactive visualization is one of the methods where users can decide a proper clustering parameters. In this paper, a new clustering approach called CDCS (Categorical Data Clustering with Subjective factors) is introduced, where a visualization tool for clustered categorical data is developed such that the result of adjusting parameters is instantly reflected. The experiment shows that CDCS generates high quality clusters compared to other typical algorithms.     

Hybrid fixed point theory for strictly monotone increasing multi-valued mappings with applications

In this paper, a general hybrid fixed point theorem for the strict monotone increasing multi-valued mappings in ordered Banach spaces is proved via measure of noncompactness and it is further applied to perturbed functional nonconvex differential inclusions for proving the existence results for the extremal solutions under mixed Lipschitz, compactness and strict monotonic conditions.     

Hybrid algorithm for generalized mixed equilibrium problems and variational inequality problems and fixed point problems

In this paper, we introduce a new iterative algorithm by hybrid method for finding a common element of the set of solutions of finite general mixed equilibrium problems and the set of solutions of a general variational inequality problem for finite inverse strongly monotone mappings and the set of common fixed points of infinite family of strictly pseudocontractive mappings in a real Hilbert space. Then we prove strong convergence of the scheme to a common element of the three above described sets. Our result improves and extends the corresponding results announced by many others.