新的Cauchy-Schwarz距离函数与多模态医学图像配准

信息论测度,特别是Shannon互信息是多模态图像配准的一种重要方法,但除了互信息之外,仍然存在其它的函数来实现这一任务。对互信息、Kullback-Leibler距离和Shannon不等式之间相互关系作了分析,根据这些关系和不等式理论,提出了新的Cauchy-Schwarz距离,并将这一距离测度用于多模态医学图像的配准处理。从计算速度、噪声容忍性、测度函数图形的特点和图像窗口大小影响等几个方面,通过MR和PET医学图像的实验分析,对新的Cauchy-Schwarz距离测度和典型的Shannon信息论测度进行了分析比较。实验结果表明,新的Cauchy-Schwarz距离测度函数同样可以用于多模态图像配准,而且有着更强的噪声容忍性和更为节省的计算量。     

基于算术-几何均值距离的多模态图像配准

根据图像灰度联合概率分布函数与图像相似程度之间的关系,提出了一种基于算术 几何均值距离的多模态图像配准新测度。与基于信息论的测度不同,新测度不再要求概率分布必须满足连续性的要求。实验结果表明,所提出的新测度比基于信息论的测度具有更强的噪声鲁棒性和计算量更小。     

基于尺度不变特征变换和区域互信息优化的多源遥感图像配准

为了进一步提高遥感图像配准精度, 提出了尺度不变特征变换(SIFT)结合区域互信息优化的遥感图像配准方法. 首先利用混沌序列的随机性和遍历性, 提出一种混沌量子粒子群优化(CQPSO)算法, 在量子粒子群优化(QPSO)算法迭代陷入早熟收敛时, 采用一种新的机理引入混沌序列, 进化粒子克服早熟. 图像配准算法分为预配准和精配准两个过程. 基于SIFT算法提取特征点, 经匹配和有效地外点排除完成预配准, 然后对匹配特征点坐标进行亚像素级微调, 通过最小二乘法求得一系列匹配参数构造初始粒子群, 最后利用混沌量子粒子群优化区域互信息完成精配准, 得到最优匹配参数. 用一些标准测试函数对所提出的CQPSO和QPSO及粒子群优化(PSO)算法进行了实验比较, 另外, 对SIFT, SIFT结合PSO算法优化区域互信息, SIFT结合QPSO算法优化区域互信息和SIFT结合CQPSO算法优化区域互信息(SRC)等四种算法进行了不同分辨率遥感图像配准实验比较和不同时相遥感图像配准实验比较, 实验结果验证了所提出的CQPSO算法的优越性和SRC配准方法的有效性.     

基于最优Atlas多模态图像的非刚性配准分割算法

提出了一种基于最优Atlas图像搜索和局部加权B样条变换的全自动非刚性分层配准分割感兴趣区域(ROI)方法。实验结果表明,所提算法配准的ROI准确度达到95.6%,归一化互信息值为1.8432,均方根误差为1.12%,相关系数提高了18.33%。相比其他配准方法,所提方案的配准精度及准确度明显提升,对临床辅助诊断有重要意义。     

一种结合梯度方向互信息和多分辨混合优化的多模图像配准方法

针对基于传统互信息图像配准容易产生局部极大值,同时结合梯度信息的互信息改进方法不能很好地应用于梯度幅值差异较大的多模图像配准,提出了一种新的结合梯度方向的互信息测度函数.在参量优化过程中,将具有全局优化的遗传算法和Powell局部优化算法动态结合,前者的配准结果为后者的算法优化提供有效的初始点以抑制局部极值,同时借鉴小波变换中多分辨率的思想,在低分辨率图像中粗略配准后,上升到高分辨率图像上进一步细化配准结果,增加算法鲁棒性并减少优化时间.多幅红外与可见光图像配准实验结果证明,提出的算法具有配准精度高和鲁棒性强等特点.     

基于混合Powell法的太赫兹/可见光双波段图像配准

提出一种改进Powell法结合遗传算法的混合算法,配准人体安检仪的太赫兹与可见光双波段图像。针对被动式太赫兹图像特征点难以提取的问题,以互信息为相似性测度,通过遗传算法寻优获取7个仿射变换参数的近似解并将其设置为改进Powell算法的初始点,然后利用改进Powell法的局部搜索能力,得到7个变换参数的精确解。利用单一遗传算法、单一Powell算法和本文混合算法分别对两组太赫兹和可见光图像进行配准。以均方根误差为评价标准对配准结果进行评价。实验结果证明,相比于单一遗传算法和Powell算法,该方法达到了更高的配准精度。能进一步用于太赫兹与可见光双波段图像融合,以提高对衣物内藏匿物品的识别精度,增强太赫兹人体安检仪的实用性。     

同步辐射双能CT图像的高精度配准研究

对双能计算机断层扫描(CT)图像进行三维图像配准是准确获取样品内部元素分布信息的前提。针对同步辐射双能CT成像存在的图像空间失准问题,提出了一种以互信息作为衡量双能CT图像相似度的图像配准方法。采用部分体积(PV)插值算法计算互信息,采用遗传算法进行较优参数的搜索,并以该参数作为Powell算法搜索的起始点,结合基于Hanning窗的部分体积插值(HPV)算法搜索得到最佳配准参数。数值模拟验证了HPV算法的优越性,同时配准精度达到亚像素级。实验结果表明,该方法能够有效配准双能CT图像并获得三维元素分布信息。     

基于聚类分割和形态学的可见光与SAR图像配准

针对可见光与SAR图像灰度差异大,共有特征提取难的问题,提出了一种基于k-均值聚类分割和形态学处理的轮廓特征配准方法。利用k-均值聚类算法对两类图像进行分割,得到图像分割区域;通过形态学处理,有效减少SAR图像斑点噪声影响,准确提取两类图像的封闭轮廓;采用轮廓不变矩理论,引入矩变量距离均值、方差约束机制和一致性检查的匹配策略,获取最佳匹配对,实现了两类图像的配准。通过实验,三组图像的配准精度分别达到0.3450、0.2163和0.1810,结果表明该法可行且能达到亚像素的配准精度。     

基于心脏电影磁共振图像的一种新的右心室多图谱分割方法

基于心脏电影磁共振图像的右心室(RV)分割,对心脏疾病的诊疗及预后有着十分重要的意义.右心室结构复杂,传统图像分割方法始终未能达到较高的精度.多图谱方法通过配准和融合来实现RV分割,是近几年RV分割中的主要方法之一.本文提出一种新的右心室多图谱分割方法,能够实现RV的全自动准分割.本文首先采用自适应仿射传播算法获取一系列图谱集,并基于豪斯多夫距离和归一化互信息选择与目标图像最相似的图谱集;然后,依次采用多分辨率的仿射变换和Diffeomorphic demons算法将目标图像配准到最相似图谱集,并将配准得到的形变场应用于标记图像获得粗分割结果;最后,本文采用COLLATE算法融合粗分割结果得到RV轮廓.30例心脏电影磁共振数据被用于回顾性分析.本文算法与放射专家手工分割的RV相比,Dice指标和豪斯多夫距离的平均值分别为0.84,11.46 mm;舒张末期容积,收缩末期容积,射血分数的相关系数和偏差均值分别是0.94,0.90,0.86;2.5113,–3.4783,0.0341.与卷积神经网络相比,本文算法在收缩末期的分割精度更接近手动分割结果.实验结果表明,该方法从有效的图谱选择和基于多分辨率的Diffeomorphic demons算法的多级配准提高了右心室分割的精度,有望应用于临床辅助诊断.     

基于小波变换和混合遗传算法的医学图像配准

为了解决灰度图像配准中由于目标函数容易陷入局部极值而造成的误匹配问题,使参数随图像的NMI计算和多分辨率级数进行自适应调整,采用基于小波变换多分辨率策略,形成多尺度匹配模型,并将粒子群算法(PSO)作为添加算子,提出了以图像归一化互信息(NMI)作为相似性测度的混合遗传算法,对CT与MRI图像进行了配准。实验结果表明,该方法能够解决遗传算法早熟收敛问题,有效地克服信息函数的局部极值,实现图像的自动配准,具有匹配精确、鲁棒性好及效率高等优点。     

公路交通噪声等效车道距离的确定方法探讨

在公路交通噪声计算中,目前常用的确定等效车道距离的方法是计算接受点至近车道和远车道的距离的几何平均值,但使用该方法得到的接受点处的交通噪声与使用两个车道分别计算后再叠加的方法所得到结果相比,存在一定偏差。本文提出用调和平均值确定等效车道距离,即用接受点至近车道和远车道的距离的几何平均值的平方除以算术平均值来确定等效车道距离。通过比较,证明调和平均法比几何平均法具有更高的精确性。     

Co-registration method for photoacoustic imaging and laser speckle imaging

Combining photoacoustic (PA) imaging with laser speckle (LS) imaging (LSI) can simultaneously determine total hemoglobin concentration (HbT), hemoglobin oxygen saturation (SO2), and blood flow rates. Thus,the co-registration of PA and LS images is important in physiological studies and pathological diagnosis.This letter presents a co-registration algorithm combining mutual information with the maximum betweenclass variance segmentation method (Otsu method). The mutual information and Otsu method are used to provide the registration measure criterion and image feature recognition, respectively. The evaluation results show that the registration function possesses a single maximum peak and high smoothness across the global co-registration district, indicating a robust behavior. Moreover, this method has good registration accuracy, and the fusion result simultaneously visualizes the separate functional information of two kindsof images.     

Nonrigid image registration using similarity measure based on spatially weighted global probability distribution function

In nonrigid image registration, similarity measures including spatial information have been shown to perform better than those measures without spatial information. In this work, we provide new insight to the relationships among regional mutual information, regional probability distribution functions (PDFs) and global PDFs, and propose a novel nonrigid registration scheme with spatially weighted global probability distribution function (SWGPDF). Similarity measures based on SWGPDF (SWGPDFSM) are constructed. Three different spatial sub-region division methods are compared: the equally spaced sub-region (ESSR), the local binary pattern sub-region (LBPSR) and the gradient sub-region (GSR). The registration scheme applies B-spline based free form deformations (FFDs) as the transformation model. A Parzen window and linear interpolation are used to construct histograms. The SWGPDFSM registration scheme with ESSR space division is compared with the traditional global mutual information (gMI), the traditional global normalized mutual information (gNMI), regional mutual information and the SWGPDFSM with LBPSR or GSR space division. The test results show that SWGPDFSM scheme with ESSR space division outperforms the other schemes for elastically aligning images in the presence of big geometrical transformations, bias fields and illumination changes.     

Interpreting Social Accounting Matrix (SAM) as an Information Channel

Information theory, and the concept of information channel, allows us to calculate the mutual information between the source (input) and the receiver (output), both represented by probability distributions over their possible states. In this paper, we use the theory behind the information channel to provide an enhanced interpretation to a Social Accounting Matrix (SAM), a square matrix whose columns and rows present the expenditure and receipt accounts of economic actors. Under our interpretation, the SAM’s coefficients, which, conceptually, can be viewed as a Markov chain, can be interpreted as an information channel, allowing us to optimize the desired level of aggregation within the SAM. In addition, the developed information measures can describe accurately the evolution of a SAM over time. Interpreting the SAM matrix as an ergodic chain could show the effect of a shock on the economy after several periods or economic cycles. Under our new framework, finding the power limit of the matrix allows one to check (and confirm) whether the matrix is well-constructed (irreducible and aperiodic), and obtain new optimization functions to balance the SAM matrix. In addition to the theory, we also provide two empirical examples that support our channel concept and help to understand the associated measures.     

Refined Young Inequality and Its Application to Divergences

We give bounds on the difference between the weighted arithmetic mean and the weighted geometric mean. These imply refined Young inequalities and the reverses of the Young inequality. We also studied some properties on the difference between the weighted arithmetic mean and the weighted geometric mean. Applying the newly obtained inequalities, we show some results on the Tsallis divergence, the Rényi divergence, the Jeffreys–Tsallis divergence and the Jensen–Shannon–Tsallis divergence.     

一种基于边缘特征的亚像素投影配准算法

为了提高红外图像配准准确度,充分利用图像的边缘信息,结合线性插值,对原有的投影算法引入新的图像相似性评价函数——均方误差函数(Mean Sequare Erron,MSE),提出了基于边缘特征的亚像素投影配准算法.提取图像边缘特征明显的部分做配准能提高匹配概率,引入积分投影能有效降低算法的计算复杂度.算法中还运用了线性插值,使配准准确度达到亚像素级水平.     

Study of mutual information multimodality medical image registration based on modified simplex optimization method

Because of a different imaging mechanism and highly complexity of body tissues and structures. Different modality medical images provide non-overlay complementary information. This has very important significance for multimodal medical image registration. Image registration is the first and key part of problem to be solved in the integrations. When the spatial position of two medical images is same, the registration could be achieved. For two CT and PET images, the principal axis method is adopted to achieve the rough registration. The modified simplex algorithm is employed to implement global search using the mutual information as similarity measure. The initial registration parameters are achieved through principal axis Based on the results of test, improved simplex method can adjust reflecting distance. Stepped-up optimization algorithm on the new experimental points through the methods of “reflection”, “enlargement”, “shrinkage” or “global systolic”. A mutual information registration based on modified simplex optimization method is presented in this paper to improve the speed of medical image registration.Results indicate that the proposed registration method prevents the optimizing process from falling into local extremum and improves the convergence speed while keeping the precision. The accurate registration of multimodal image with different resolutions is achieved.