参数合成空间变换网络的遥感图像一致性配准

目的 遥感图像配准是对多组图像进行匹配和叠加的过程。该技术在地物检测、航空图像分类和卫星图像融合等方面发挥着重要作用，主要有传统方法和基于深度学习的方法。其中，传统遥感图像配准算法在进行配准时会耗费大量人力，并且运行时间过长。而基于深度学习的遥感图像配准算法虽然减少了人工成本，提高了模型自适应学习的能力，但是算法的配准精度和运行时间仍有待提高。针对基于深度学习的配准算法存在的问题，本文提出了参数合成的空间变换网络对遥感图像进行双向一致性配准。方法 通过增加空间变换网络的深度、合成网络内部的参数对空间变换模型进行改进，并将改进后的模型作为特征提取部分的骨干网络，有效地提高网络的鲁棒性。同时，将单向配准方法改为双向配准方法，进行双向的特征匹配和特征回归，保证配准方向的一致性。然后将回归得到的双向参数加权合成，提高模型的可靠性和准确性。结果 将本文实验结果与两种经典的传统方法SIFT（scale-invariant feature transform）、SURF（speeded up robust features）对比，同时与近3年提出的CNNGeo（convolutional neural network architecture for geometric matching）、CNN-Registration（multi-temporal remote sensing image registration）和RMNet（robust matching network）3种最新的方法对比，配准结果表明本文方法不仅在定性的视觉效果上较为优异，而且在定量的评估指标上也有不错的效果。在Aerial Image Dataset数据集上，本文使用"关键点正确评估比例"与以上5种方法对比，精度分别提高了36.2%、75.9%、53.6%、29.9%和1.7%；配准时间分别降低了9.24 s、7.16 s、48.29 s、1.06 s和4.06 s。结论 本文所提出的配准方法适用于时间差异变化（多时相）、视角差异（多视角）与拍摄传感器不同（多模态）的3种类型的遥感图像配准应用。在这3种类型的配准应用下，本文算法具有较高的配准精度和配准效率。     

梯度与曲率变形力相结合的图像配准方法——大形变非刚性图像配准

Active Demons算法是Demons算法的改进形式,其将形变配准视作扩散问题,利用牛顿作用力与反作用力思想,仅依靠梯度信息确定浮动图像的位移,在处理大形变配准问题时存在配准精度不高的弊病。将等照度线曲率作为一个控制形变的驱动力因素引入Active Demons扩散方程,建立了一个具有梯度与曲率双重驱动力相结合的非线性扩散模型（Active G&C model）,并在Active G&C模型应用于大形变图像配准的算法实现过程中加入多分辨率策略,以提高大形变图像的配准精度。实验结果表明,这一模型较经典的Active Demons算法具有更好的配准性能。     

基于GPU的快速三维医学图像刚性配准技术*

自动三维配准将多个图像数据映射到同一坐标系中,在医学影像分析中有广泛的应用。但现有主流三维刚性配准算法(如FLIRT)速度较慢,2563大小数据的刚性配准需要300 s左右,不能满足快速临床应用的需求。为此提出了一种基于CUDA(compute unified device architecture)架构的快速三维配准技术,利用GPU(gra-phic processing unit)并行计算实现配准中的坐标变换、线性插值和相似性测度计算。临床三维医学图像上的实验表明,该技术在保持配准精度的前提下将速度提     

基于深度卷积特征光流的形变医学图像配准算法

光流法是一种基于光流场模型的重要而有效的形变配准算法。针对现有光流法所用特征质量不高使得配准结果不够准确的问题，将深度卷积神经网络特征和光流法相结合，提出了基于深度卷积特征光流（DCFOF）的形变医学图像配准算法。首先利用深度卷积神经网络稠密地提取图像中每个像素所在图像块的深度卷积特征，然后基于固定图像和浮动图像间的深度卷积特征差异求解光流场。通过提取图像的更为精确和鲁棒的深度学习特征，使求得的光流场更接近真实形变场，提升了配准精度。实验结果表明，所提算法能够更有效地解决形变医学图像配准问题，其配准精度优于Demons算法、尺度不变特征变换(SIFT) Flow算法以及医学图像专业配准软件Elastix。     

A multi-resolution area-based technique for automatic multi-modal image registration

To allow remotely sensed datasets to be used for data fusion, either to gain additional insight into the scene or for change detection, reliable spatial referencing is required. With modern remote sensing systems, reliable registration can be gained by applying an orbital model for spaceborne data or through the use of global positioning (GPS) and inertial navigation (INS) systems in the case of airborne data. Whilst, individually, these datasets appear well registered when compared to a second dataset from another source (e.g., optical to LiDAR or optical to radar) the resulting images may still be several pixels out of alignment. Manual registration techniques are often slow and labour intensive and although an improvement in registration is gained, there can still be some misalignment of the datasets. This paper outlines an approach for automatic image-to-image registration where a topologically regular grid of tie points was imposed within the overlapping region of the images. To ensure topological consistency, tie points were stored within a network structure inspired from Kohonen’s self-organising networks [24]. The network was used to constrain the motion of the tie points in a manner similar to Kohonen’s original method. Using multiple resolutions, through an image pyramid, the network structure was formed at each resolution level where connections between the resolution levels allowed tie point movements to be propagated within and to all levels. Experiments were carried out using a range of manually registered multi-modal remotely sensed datasets where known linear and non-linear transformations were introduced against which our algorithm’s performance was tested. For single modality tests with no introduced transformation a mean error of 0.011 pixels was identified increasing to 3.46 pixels using multi-modal image data. Following the introduction of a series of translations a mean error of 4.98 pixels was achieve across all image pairs while a mean error of 7.12 pixels was identified for a series of non-linear transformations. Experiments using optical reflectance and height data were also conducted to compare the manually and automatically produced results where it was found the automatic results out performed the manual results. Some limitations of the network data structure were identified when dealing with very large errors but overall the algorithm produced results similar to, and in some cases an improvement over, that of a manual operator. We have also positively compared our method to methods from two other software packages: ITK and ITT ENVI.     

基于多通道稀疏编码的非刚性多模态医学图像配准

针对稀疏编码相似性测度在非刚性医学图像配准中对灰度偏移场具有较好的鲁棒性,但只适用于单模态医学图像配准的问题,提出基于多通道稀疏编码的非刚性多模态医学图像配准方法。该方法将多模态配准问题视为一个多通道配准问题来解决,每个模态在一个单独的通道下运行;首先对待配准的两幅图像分别进行合成和正则化,然后划分通道和图像块,使用K奇异值分解（K-SVD）算法训练每个通道中的图像块得到分析字典和稀疏系数,并对每个通道进行加权求和,采用多层P样条自由变换模型来模拟非刚性几何形变,结合梯度下降法优化目标函数。实验结果表明,与局部互信息、多通道局部方差和残差复杂性（MCLVRC）、多通道稀疏诱导的相似性测度（MCSISM）、多通道Rank Induced相似性测度（MCRISM）多模态相似性测度相比,均方根误差分别下降了30.86%、22.24%、26.84%和16.49%。所提方法能够有效克服多模态医学图像配准中灰度偏移场对配准的影响,提高配准的精度和鲁棒性。     

Multisensor image registration via implicit similarity

This paper presents an approach to the registration of significantly dissimilar images, acquired by sensors of different modalities. A robust matching criterion is derived by aligning the locations of gradient maxima. The alignment is achieved by iteratively maximizing the magnitudes of the intensity gradients of a set of pixels in one of the images, where the set is initialized by the gradient maxima locations of the second image. No explicit similarity measure that uses the intensities of both images is used. The computation utilizes the full spatial information of the first image and the accuracy and robustness of the registration depend only on it. False matchings are detected and adaptively weighted using a directional similarity measure. By embedding the scheme in a "coarse to fine" formulation, we were able to estimate affine and projective global motions, even when the images were characterized by complex space varying intensity transformations. The scheme is especially suitable when one of the images is of considerably better quality than the other (noise, blur, etc.). We demonstrate these properties via experiments on real multisensor image sets.     

融合密集残差块和GAN变体的医学图像非刚性配准

目的 现有的医学图像配准算法在处理较大非刚性形变的医学图像时,存在配准精度低和泛化能力不足的问题。针对该问题,提出结合密集残差块和生成对抗网络（generative adversarial network,GAN）的图像配准方法,用于多模态医学图像的非刚性配准。方法 将密集残差块引入到生成器,提取待配准图像对的更多高层特征,从而提高配准精度;在GAN中采用最小二乘损失代替逻辑回归构造的交叉熵损失,由于最小二乘损失的收敛条件更严格,同时能缓解梯度消失和过拟合,从而提高配准模型的稳定性;在判别器网络中引入相对平均GAN（relative average GAN,RaGAN）,即在标准判别器的基础上增加一项梯度惩罚因子,降低判别器的判别误差,从而相对减少配准模型的配准误差,有助于稳定配准精度。结果 在DRIVE（digital retinal images for vessel extraction）数据集上进行训练和验证,在Sunybrook Cardiac数据集和Brain MRI数据集上进行泛化性能测试,并与几种主流医学图像非刚性配准算法进行对比实验,表明,本文配准模型在精度和泛化能力上均有一定程度的提升,相比其他方法,视网膜图像、心脏图像和脑部图像的配准Dice值分别提升了3.3%、3.0%、1.5%。结论 提出的配准方法能够获取更多高层特征信息,从而提升配准精度;同时基于最小二乘法构建损失函数并对判别器进行加强,能够使得配准模型在训练阶段快速收敛,提高了配准模型的稳定性和泛化能力,适合存在较大非刚性形变的医学图像的配准。     

Fingerprint image mosaicking by recursive ridge mapping.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.     

Dynamic meshing for deformable image registration

Finite element method (FEM) is commonly used for deformable image registration. However, there is no existing literature studying how the superimposed mesh structure would influence the image registration process. We study this problem in this paper, and propose a dynamic meshing strategy to generate mesh structure for image registration. To construct such a dynamic mesh during image registration, three steps are performed. Firstly, a density field that measures the importance of a pixel/voxel’s displacement to the registration process is computed. Secondly, an efficient contraction–optimization scheme is applied to compute a discrete Centroidal Voronoi Tessellation of the density field. Thirdly, the final mesh structure is constructed by its dual triangulation, with some post-processing to preserve the image boundary. In each iteration of the deformable image registration, the mesh structure is efficiently updated with GPU-based parallel implementation. We conduct experiments of the new dynamic mesh-guided registration framework on both synthetic and real medical images, and compare our results with the other state-of-the-art FEM-based image registration methods.     

基于视觉特征的多传感器图像配准

多传感器图像配准在空间图像处理中有非常重要的应用价值,但同时也面临着多源空间数据各异性困难。考虑到图像配准过程中的多分辨率视觉特征,采用基于小波的多分辨率图像分解来指导从粗到细的配准过程,利用扩展的轮廓跟踪算法提取满足视觉特征的轮廓,在轮廓链码曲率函数的基础上实现基于傅里叶变换的多分辨率形状特征匹配。与已有的基于特征的图像配准算法进行实验比较,实验结果表明该方法对于从多传感器得到的异质图像具有良好的配准效果。     

Image fusion enhancement of deformable human structures using a two-stage warping-deformable strategy: A content-based image retrieval consideration

In medical image registration and content-based image retrieval, the rigid transformation model is not adequate for anatomical structures that are elastic or deformable. For human structures such as abdomen, registration would involve global features such as abdominal wall as well as local target organs such as liver or spleen. A general non-rigid registration may not be sufficient to produce image matching of both global and local structures. In this study, a warping-deformable model is proposed to register images of such structures. This model uses a two-stage strategy for image registration of abdomen. In the first stage, the global-deformable transformation is used to register the global wall. The warping-transformation is used in second stage to register the liver. There is a good match of images using the proposed method (mean similarity index = 0.73545).The image matching correlation coefficients calculated from eight pairs of CT and MR images of abdomen indicates that the warping-deformable transformation gives better matching of images than those without transformation (p < 0.001, paired t-test). This study has established a model for image registration of deformable structures. This is particularly important for data mining of image content retrieval for structures which are non-rigid. The result obtained is very promising but further clinical evaluation is needed     

改进的逆向组合算法在图像配准中的应用

逆向组合算法在进行图像配准时精度很高,但对模板图像在待配准图像中的初始位置敏感。针对该问题,提出一种改进的逆向组合算法,通过计算巴氏系数进行模板匹配,实现输入图像的粗定位后,应用逆向组合算法进行精确定位。实验结果证明,改进的算法对图像发生形变的情况具有较好的鲁棒性,相比原算法,配准能力更强。     

全局一致的多视图像配准

为了提高多视图像配准的精度和一致性,考虑到各种不确定信息,提出了一个基于概率估计的多视图像配准方法。把各视点的位置姿态作为系统状态,构造系统增广模型和系统观测模型,运用增广卡尔曼滤波技术对各视点的位置姿态进行递归地估计,实现了精确的、全局一致的多视图像配准。实验对提出的配准方法进行了验证,结果表明提出的多视图像配准方法能够有效地处理不确定信息,配准结果是一致的和精确的。     

基于FCN和互信息的医学图像配准技术研究

针对传统配准方法在进行三维多模态图像配准时存在收敛速度较慢、容易陷入极值等问题,提出一种基于全卷积神经网络（Fully Convolutional Networks,FCN）和互信息的配准方法。利用FCN模型提取二维图像深层特征并进行粗配准;将得到的配准结果作为互信息算法的初始搜索点,从而使搜索范围缩小至全局最优解附近;利用互信息算法对参数进一步微调优化,得到最优三维配准结果。实验结果表明,在进行CT-MR图像配准时,所提方法不仅可以大幅度提升配准速度,还能有效避免局部收敛的情况,具有更高的准确性。     

Fast deformable matching of 3D images over multiscale nested subspaces. Application to atlas-based MRI segmentation

This paper presents a fast method to perform dense deformable matching of 3D images, applied to the registration of inter-subject brain MR images. To recover the complex morphological variations in neuroanatomy, the registration method uses a hierarchy of 3D deformations fields that are estimated, by minimizing a global energy function over a sequence of nested subspaces. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR images from different individuals. An application of the deformable image matching method to 3D atlas-based image segmentation is presented. This atlas-based segmentation is used at Strasbourg Hospital, in daily clinical applications, in order to extract regions of interest from 3D MR images of patients suffering from epilepsy.     

变参数active demons算法下的多通道弥散张量图像配准

目的 弥散张量图像（DTI）配准不仅要保证配准前后图像解剖结构的一致性，还要保持张量方向的一致性。demons算法下的多通道DTI配准方法可充分利用张量的信息，改善配准质量，但大形变区域配准效果不理想，收敛速度慢。active demons算法能够加快收敛速度，但图像的拓扑结构容易改变。由此提出一种变参数active demons算法下的多通道DTI配准方法。方法 综合active demons算法中平衡系数能加快收敛速度、均化系数能提高DTI配准精度的优点，手动选择一个均化系数，并在算法收敛过程中随着高斯核的减小动态调整平衡系数。在配准开始时采用较小的平衡系数获得较快的收敛速度，随着收敛的加深逐渐增大平衡系数获得较小的配准误差。结果 active demons方法能改善DTI大形变区域的配准问题，但均化系数太小会改变图像拓扑结构。固定均化系数，引入单一的平衡系数能加快收敛速度，但会导致拓扑结构改变。变参数active demons方法有效提高了配准的收敛速度，明显改善大形变区域的配准效果，同时能保持图像拓扑结构不变。变参数active demons配准后的10组数据均获得最小均方差（MSE）和最大特征值特征向量对重叠率（OVL），配准精度最高。在0.05的配对样本t检验水平下，变参数active demons和active demons方法配准后的MSE、OVL的差异均有统计学意义；变参数active demons和demons方法配准后的MSE、OVL的差异均有统计学意义（p<0.05）。结论 变参数active demons算法下的多通道DTI配准方法明显提高了配准精度和速度，改善了demons方法不能有效配准大形变区域的问题，同时能够保持配准前后图像的拓扑结构，尤其适合个体间形变较大的DTI配准。     

Applicability of the SIFT operator to geometric SAR image registration

The SIFT operator's success for computer vision applications makes it an attractive alternative to the intricate feature based SAR image registration problem. The SIFT operator processing chain is capable of detecting and matching scale and affine invariant features. For SAR images, the operator is expected to detect stable features at lower scales where speckle influence diminishes. To adapt the operator performance to SAR images we analyse the impact of image filtering and of skipping features detected at the highest scales. We present our analysis based on multisensor, multitemporal and different viewpoint SAR images. The operator shows potential to become a robust alternative for point feature based registration of SAR images as subpixel registration consistency was achieved for most of the tested datasets. Our findings indicate that operator performance in terms of repeatability and matching capability is affected by an increase in acquisition differences within the imagery. We also show that the proposed adaptations result in a significant speed-up compared to the original SIFT operator.     

Feature-based registration of histopathology images with different stains: An application for computerized follicular lymphoma prognosis

Follicular lymphoma (FL) is the second most common type of non-Hodgkin’s lymphoma. Manual histological grading of FL is subject to remarkable inter- and intra-reader variations. A promising approach to grading is the development of a computer-assisted system that improves consistency and precision. Correlating information from adjacent slides with different stain types requires establishing spatial correspondences between the digitized section pair through a precise non-rigid image registration. However, the dissimilar appearances of the different stain types challenges existing registration methods.This study proposes a method for the automatic non-rigid registration of histological section images with different stain types. This method is based on matching high level features that are representative of small anatomical structures. This choice of feature provides a rich matching environment, but also results in a high mismatch probability. Matching confidence is increased by establishing local groups of coherent features through geometric reasoning. The proposed method is validated on a set of FL images representing different disease stages. Statistical analysis demonstrates that given a proper feature set the accuracy of automatic registration is comparable to manual registration.     

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Cardiovascular disease is the leading cause of death worldwide and for this reason computer-based diagnosis of cardiac diseases is a very important task. In this article, a method for segmentation of aortic outflow velocity profiles from cardiac Doppler ultrasound images is presented. The proposed method is based on the statistical image atlas derived from ultrasound images of healthy volunteers. The ultrasound image segmentation is done by registration of the input image to the atlas, followed by a propagation of the segmentation result from the atlas onto the input image. In the registration process, the normalized mutual information is used as an image similarity measure, while optimization is performed using a multiresolution gradient ascent method. The registration method is evaluated using an in-silico phantom, real data from 30 volunteers, and an inverse consistency test. The segmentation method is evaluated using 59 images from healthy volunteers and 89 images from patients, and using cardiac parameters extracted from the segmented image. Experimental validation is conducted using a set of healthy volunteers and patients and has shown excellent results. Cardiac parameter segmentation evaluation showed that the variability of the automated segmentation relative to the manual is comparable to the intra-observer variability. The proposed method is useful for computer aided diagnosis and extraction of cardiac parameters.