Hierarchical Non-linear Image Registration Integrating Deformable Segmentation

IntroductionSegmentation and registration are two impor-tant problems in the field of medical image analy-sis. Traditionally, solutions have been developedfor each of these two problems in relative isolationfrom the other, but with increasing dependence onthe existence of a solution for the other. For exam-ple, model-based segmentation methods[1,2]need toregister individual data sets to a common frame ofreference, so that statistics about the models canbe gathered to guide the evolution of the…     

Deformable Registration of MR Brains with Best Features

Introduction Deformable registration is very important formedical image analysis and so far various methodshave been proposed in decades[1-4]. HAMMER reg-istration algorithm[5]defines an attribute vector in-cluding intensity, edge type, and geometric mo-ment invariants as a signature of each point, to re-duce ambiguity in correspondence matching duringthe image registration. Another characteristic ofHAMMER is the hierarchical matching mecha-nism, which helps avoid the warping being trapped…     

Mutual information optimization based dynamic log-polar image registration

Log-polar transformation(LPT)is widely used in image registration due to its scale and rotation invariant properties.Through LPT,rotation and scale transformation can be made into translation displacement in log-polar coordinates,and phase correlation technique can be used to get the displacement.In LPT based image registration,constant samples in digitalization processing produce less precise and effective results.Thus,dynamic log-polar transformation(DLPT)is used in this paper.DLPT is a method that generates several sample sets in axes to produce several results and only the effective results are used to get the final results by using statistical approach.Therefore,DLPT can get more precise and effective transformation results than the conventional LPT.Mutual information(MI)is a similarity measure to align two images and has been used in image registration for a long time.An optimal transform for image registration can be obtained by maximizing MI between the two images.Image registration based on MI is robust in noisy,occlusion and illumination changing circumstance.In this paper,we study image registration using MI and DLPT.Experiments with digitalizing images and with real image datasets are performed,and the experimental results show that the combination of MI with DLPT is an effective and precise method for image registration.     

基于点特征和边缘特征的无人机影像配准方法

为解决变形较大的无人机影像配准问题,提出了点特征和边缘特征相结合的配准方法.用尺度不变特征变换(SIFT)算法提取点特征,完成影像的初步配准,并通过多项式函数对影像进行粗校正.在此基础上提取影像的边缘特征信息,根据距离相似性对边缘特征信息进行配准;依据色彩能量差筛选点特征信息配准结果和边缘特征信息配准结果,采用小面元微分校正的方法对变形影像进行校正.实验结果表明:提出的配准方法能够弥补点特征配准方法和边缘特征配准方法的不足,其配准的鲁棒性提高10%左右,可以较好地完成变形较大的无人机影像配准.      

基于脆弱数字水印的人脸图像的安全性

针对人脸识别系统中人脸图像数据可能受到攻击，导致人脸图像被恶意添加、替换、篡改等安全隐患，采用脆弱数字水印方法，解决上述人脸识别系统中人脸图像数据安全问题．用人脸原始图像的小波低频系数的高7位生成低频压缩图像，作为水印．通过混沌置乱加密，将水印直接嵌入到原始人脸图像的最低有效位平面，区分人脸图像篡改并定位检测．实验结果表明，采用脆弱数字水印的人脸图像对恶意添加、替换、篡改都有很高的敏感度．嵌入脆弱数字水印对人脸图形的特征提取的识别率以及识别速度的影响都很小．     

MUTUAL INFORMATION BASED 3D NON-RIGID REGISTRATION OF CT/MR ABDOMEN IMAGES

Introduction　　 Automating medical image registration is be-coming an increasingly important goal. It formsthe basis of image processing techniques to com-bine information from the different modalities,which are now frequently acquired for many pa-tients. Such combined representations of patientimages have been shown to assistin the interpreta-tion of the complementary information provided bythe modalities[1,2 ] .　　 A variety of imaging modalities gives insightto different parameters and in…     

Deformable Registration of Chest Radiographs Using B-spline Based Method with Modified Residual Complexity

Accurate registration of chest radiographs plays an increasingly important role in medical applications.However, most current intensity-based registration methods rely on the assumption of intensity conservation that is not suitable for alignment of chest radiographs. In this study, we propose a novel algorithm to match chest radiographs, for which the conventional residual complexity(RC) is modified as the similarity measure and the cubic B-spline transformation is adopted for displacement estimation. The modified similarity measure is allowed to incorporate the neighborhood influence into variation of intensity in a justified manner of the weight, while the transformation is implemented with a registration framework of pyramid structure. The results show that the proposed algorithm is more accurate in registration of chest radiographs, compared with some widely used methods such as the sum-of-squared-differences(SSD), correlation coefficient(CC) and mutual information(MI)algorithms, as well as the conventional RC approaches.     

基于差分能量图和CGBP的微表情识别

微表情是一种不能自主控制和伪装的面部表情,其与诚信度的关系密切,具有持续时间短且难以识别的特征.为提高计算机自动识别微表情的准确性,提出一种基于差分能量图和中心化Gabor二值模式（centralized Gabor binary patterns,CGBP）的微表情识别方法.该方法首先利用差分法计算微表情序列的能量得到差分能量图,获得人脸面部肌肉相位的变化;其次将Gabor与中心二值模式CBP相结合,得到CGBP算子对能量图进行微表情的特征提取;最后利用ELM分类器进行微表情分类识别.在CASME微表情库上的实验结果表明,该方法比LBP-TOP、DTSA3、Gabor、VLBP、CBP-TOP算法更能有效地获得微表情序列的时空纹理特征,平均识别率为86.54%.      

Multi-band Image Registration Method Based on Fourier Transform

This paper presented a registration method based on Fourier transform for multi-band images which is involved in translation and small rotation. Although different band images differ a lot in the intensity and features,they contain certain common information which we can exploit. A model was given that the multi-band images have linear correlations under the least-square sense. It is proved that the coefficients have no effect on the registration progress if two images have linear correlations. Finally, the steps of the registration method were proposed. The experiments show that the model is reasonable and the results are satisfying.     

Image fusion scheme based on nonsubsampled contourlet and block-based cosine transform

A kind of nonsubsampled contourlet and block-based cosine transform (NSCBCT) is developed, and its application in image fusion is studied in this paper. The construction of filtering banks is based on the nonsubsampled contourlet transform (NSCT) and block-based discrete cosine transform (B-DCT). We combine NSCT and B-DCT to design filters that lead to NSCBCT with better singularity representation than either of them in isolation. A design framework based on the hybrid approach is proposed, which allows for the fast implementation based on NSCT and B-DCT respectively. In addition, a new image fusion scheme based on NSCBCT for multispectral and panchromatic satellite images is proposed. Firstly, because it adopts NSCT, the fused satellite images have higher spatial resolution than those based on wavelets. Secondly, based on the localized high frequency information provided by B-DCT, the proposed fusion scheme can reduce the spectral distortion of the fused image further. Experimental results show that the proposed fusion method is able to increase the spatial resolution and reduce the spectral distortion of the fused image at the same time.     

基于特征脸和LS-SVM分类器的人脸性别分类

提出使用特征脸和最小二乘支持向量机(LS-SVM)分类器相结合进行人脸性别分类.我们首先从训练图像中求得特征脸空间,然后将训练集和测试集图像投影到特征脸空间得到投影系数.使用训练样本投影系数训练LS-SVM分类器,对训练图像和测试图像进行分类试验,同时计算出分类准确率,实验结果表明LS-SVM分类要比其他分类算法有更好的优越性.在实验中我们也使用交叉验证来确定特征脸数目和核函数参数.     

Groupwise Registration of Brain Magnetic Resonance Images:A Review

A relatively new family of image registration methods, namely groupwise registration, has recently emerged and been widely investigated due to its fundamentally key role in analyzing image populations in terms of atlas-based analysis or clinical diagnostic systems. Compared with pairwise registration, groupwise registration is capable of handling a large-scale population of images simultaneously in an unbiased way. In this paper, a review of the latest research on groupwise registration is presented. First, the schemes of pairwise registration and groupwise registration are compared. Then, a classification of groupwise registration and several exemplar implementations of groupwise registration are illustrated, including their experimental results. Finally, typical applications of groupwise registration, e.g., infant atlas construction, and population-based anatomical variability evaluation, are discussed in this paper.     

基于互信息快速算法的多模医学图像配准

互信息作为相似性测度在多模医学图像配准领域得到了广泛应用,具有高精度和稳健的特点.但频繁的互信息计算降低了配准效率,同时对两幅图像重叠区域比较敏感.采用归一化互信息为测度,降低互信息计算中灰度级的快速算法,通过多模CT/MRI配准实验证明,可以在确保配准精度的同时缩短时间提高配准效率.     

基于一面两孔特征的点云配准方法

为了提高零件在扫描检测过程中点云与设计模型的配准精度,提出了一种基于一面两孔特征的点云配准方法.该方法粗配准以零件的平面/圆柱孔特征为对象,使设计模型和点云的局部坐标系重合,并通过改进ICP算法求解点云与设计模型最近点的距离最小平方和实现精配准.由于配准区域和最近点的计算方法不同,精配准进一步分为全域和特征域配准两种类型.全域精配准以距点云最近的设计模型三角网格点或投影点为最近点,适合于毛坯件;特征域精配准则通过求解点云在平面/圆柱孔特征上的投影点为最近点,适合于成品件.试验及计算结果表明:全域配准的配准精度随表面离散点距离的减小而提高, 当离散点距离达到1.50 mm时,其配准精度已经达到0.15 mm,基本满足工程应用要求.当配准精度相同时,配准效率较其它方法提高10%~20%.      

基于贝叶斯决策的航空影像自动配准

为消除像点投影差对航空影像目标监测中影像配准的影响,提出了一种基于贝叶斯决策理论的像点投影差消除方法.该方法通过推导分析像点投影差的分布规律及其对影像配准的影响,设置训练样本区,提取大量特征匹配对,计算得到投影差分布参数,并基于贝叶斯决策理论构建了像点投影差消除准则.为验证其有效性,选用KIT AIS影像数据进行了实验,比较分析了投影差消除前后的配准影像结果.分析结果显示,消除后差分视觉效果得到改善,信息熵减小10%,表明本文方法可有效提高航空影像配准的精度.      

基于联合投票网络的交通场景天气分类方法

基于交通视频监控图像的天气识别已经成为智能交通系统中重要的研究课题. 虽然卷积神经网络（convolutional neural network,CNN）在图像识别技术获得了巨大的发展,但是针对复杂交通场景的天气识别问题,现有的模型在特征表达方面仍然面临着巨大的挑战. 为了提取丰富的语义特征,提出了基于联合投票机制的深度神经网络（deep neural network,DNN）模型. 所提出的模型包括两个核心模块:基于通道和空间注意力机制的二阶特征模块和基于复合特征结果联合投票机制的分类模块, 用以提取不同天气图像中的判别性信息,提高在复杂交通场景下的天气识别性能. 最后,在两个基准天气分类数据集上进行了验证试验,结果表明:对于复杂场景条件下的天气识别问题,所提出的基于联合投票机制的深度神经网络模型的识别正确率优于目前最好的天气识别方法的1.97%.      

Eye and Iris Detection Using Projection and Radial Symmetry Transform

This paper presents an eye and iris detection algorithm for human facial images. The authors combine three features of the eye to develop the algorithm: 1) the pixels surrounding the eyes are more variable than other parts of the face; 2) eye pixels are darker than their neighbors; 3) eyes often exhibit radial symmetric properties. Through the first feature, two rough regions of both eyes are detected on the face. Eye masks are then formed based on the second feature, and a fast radial symmetry transform is applied to the two rough regions of both eyes. Finally, accurate iris centers are located by searching the maximum value of the radial symmetry transform results. Using 450 human facial images from the Caltech face database, experiments show that the success rate of the proposed method is 91.7%. The effectiveness of the method was also verified through detection of video frames.     

噪声符合高斯分布的曲面图像分割

针对噪声符合高斯分布的曲面图像分割问题,提出了一种基于变分水平集方法的隐式曲面上噪声符合高斯分布的图像分割模型。为定义隐式曲面上图像区域划分的动态轮廓线,引入另一个动态水平集函数,其零水平集与隐式曲面的交线即为曲面上区域划分的轮廓线。该模型采用N个水平集函数划分2^N个区域的区域竞争策略。通过变分方法得到图像分割空间轮廓线演化的梯度降方程。通过显式差分格式对演化方程进行离散。实验结果表明,该模型对曲面上噪声符合高斯分布的图像具有良好的分割效果,并且在图像分割的同时能够完全保持曲面固有的几何特征。     

城市政务空间信息资源目录服务体系研究

以元数据为核心的城市政务空间信息目录服务体系,可以把空间信息资源及其服务的注册、查找、使用引入一个更高的层次,使电子政务中异构GIS系统的互联互通、信息共享、业务协同成为可能,这对实现城市政务空间信息资源增值,促进资源发现、共享与利用效率具有一定的理论价值和现实意义。     

航空影像的几何校正研究与实现

航空侦察在军事上有着重要的地位,文中研究了航空影像的斜像校正.利用GPS实地采集地面控制点,采用多项式和双线性插值法对机上采集的图像进行了几何校正.校正后的图像可用于图像配准、融合和目标识别.