基于改进梯度向量流与最大互信息的图像配准

基于最大互信息的多模医学图像配准已成为医学图像处理领域的热点.低阶互信息仅关注灰度的统计特性,忽略了空间信息,因此采用图像梯度向量流的空间信息与最大互信息组合的方法来实现医学图像配准.实验表明,该方法可以大大提高配准速度和精度,降低误配准率.     

Image Similarity Based on Intensity Scaling

An image similarity measure based upon intensity scaling is investigated for the registration of images which differ in contrast. The measure is introduced for both optical flow and for finite displacements, and registration by the optical flow formulation is found to be independent of image order. While other morphological image matching methods involve differential formulations, the present work simply involves composition with scaling functions. Such rescaling is additionally found to have a smoothing effect on noisy images. Results obtained by Tikhonov regularization of scaling functions are superior to those obtained by a restricted set of basis functions. Computational results are performed for simple test cases as well as for realistic examples taken from magnetic resonance imaging. It is found that the computational cost for the implementation of scaling functions is very small, and yet the resulting image similarity can provide a match between images with smaller errors than that obtained by a sum of squared differences alone. In particular, images which are morphologically equivalent but which are scaled differently and even have different noise levels can be well matched with the scaling functions approach while the sum of squared differences would be unsuitable. In addition, matching is improved for image pairs in which contrast agent or an intensity modulation appears in one image and not in the other.

快速数字影像重建的2维/3维医学图像配准

目的 针对2D/3D医学图像配准过程中数字影像重建技术(DRR)生成图像和相似性程度测量两个步骤计算量大、耗时较长这一问题,提出了一种基于Bresenham直线生成算法改进的模式强度与梯度相结合的混合配准算法.方法 首先利用Bresenham直线生成算法原理改进传统光线投射算法(Ray-Casting),完成DRR图像的生成;其次模式强度与梯度相结合并引入多分辨率策略来降低相似性测度的计算复杂度;最终利用改进的鲍威尔优化算法对参数进行优化,完成整个配准过程.结果 实验结果表明,改进的混合配准算法与基于相关系数、互信息和模式强度的配准算法相比,配准效率大幅提升.模拟配准实验和临床配准实验完成时间分别为76.2 s和64.9 s,比传统配准算法效率提升3~6倍.结论 提出的算法在保证配准精度和高鲁棒性的前提下,大幅度地提高了2D/3D医学图像配准算法的运算速度,可以满足临床上精确引导手术进行的实时性要求.     

两种基于灰度相似性测度的超声波图像配准方法的比较

图像配准是图像分析和处理的基本问题，其在医学影像分析、遥感遥测、计算机视觉等领域有着广泛的应用。为了能稳定、准确地进行超声波图像配准，基于灰度信息的提取、变换，提出了以下两种基于灰度相似性测度的超声波图像配准方法：第1种方法是利用Harris角点检测方法提取特征点，然后由特征点提供灰度信息，其配准中的相似性测度定义为一个评价函数（cost-function）。误差评价函数；第2种方法引用了相同的评价函数，但使用的有关唯一性控制和区域对应规则是与第1种方法不同的。在给定了相似性测度的情况下，参数化的超声波图像配准可以表述为最小化的问题。第1种方法还利用多项式映射的方法来变换整幅图像，并估计了其平方和误差。实验的结果表明，这两种算法都很稳定，且合乎图像配准的要求，仅仅是第2种方法比第1种方法的性能要好一些。     

图像配准中同条件非均匀抽样原则的对比研究

使用基于浮动图像灰度概率分布和其梯度信息的非均匀抽样方法进行图像配准时,对同灰度和同梯度像素的抽样原则会影响到抽样结果和配准过程。通过对多种图像在多抽样率下进行刚体配准实验,从函数曲面和收敛性能方面,对比分析了6种数据抽样原则。实验结果表明,随机抽样原则的归一化互信息测度的收敛性能和抗噪声能力优于其他5种抽样原则。     

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.     

An iterative point correspondence algorithm for automatic image registration: an application to dental subtraction radiography

In this paper, an Automatic Iterative Point Correspondence (AIPC) algorithm towards image registration is presented. Given an image pair, distinctive points are extracted only in one of the images (reference image), and the corresponding points in the other image are obtained automatically by maximizing a similarity measure between regions of the two images with respect to the parameters of a local transformation. The maximization is accomplished by means of an iterative procedure, in which candidate solutions for the transformation parameters are tested at each iteration; these solutions are evaluated by the similarity measure between image regions. The detected point pairs by the application of the AIPC algorithm are then used to estimate the parameters of a global projective transformation for the registration of the image pair. The proposed AIPC algorithm was applied on 113 in vitro and in vivo dental image pairs providing improved registration accuracy against three widely used registration methods.     

Steganography algorithms recognition based on match image and deep features verification

Steganography algorithms recognition is a sub-section of steganalysis. Analysis shows when a steganalysis detector trained on one cover source is applied to images from an unseen source, generally the detection performance decreases. To tackle with this problem, this paper proposes a steganalytic scheme for steganography algorithms recognition. For a given testing image, a match image of the testing image is achieved. The match image is generated by performing a Gaussian filtering on the testing image to remove the possible stego signal. Then the match image is embedded in with recognized steganography algorithms. A CNN model trained on a training set is used to extract deep features from testing image and match images. Computing similarity between features with inner product operation or weighted-χ², the final decision is made according to similarity between testing feature and each class of match feature. The proposed scheme can also detect steganography algorithms unknown in training set. Experiments show that, comparing with directly used CNN model, the proposed scheme achieves considerable improvement on testing accuracy when detecting images come from unseen source.     

Ranking consistency for image matching and object retrieval

The goal of object retrieval is to rank a set of images by the similarity of their contents to those of a query image. However, it is difficult to measure image content similarity due to visual changes caused by varying viewpoint and environment. In this paper, we propose a simple, efficient method to more effectively measure content similarity from image measurements. Our method is based on the ranking information available from existing retrieval systems. We observe that images within the set which, when used as queries, yield similar ranking lists are likely to be relevant to each other and vice versa. In our method, ranking consistency is used as a verification method to efficiently refine an existing ranking list, in much the same fashion that spatial verification is employed. The efficiency of our method is achieved by a list-wise min-Hash scheme, which allows rapid calculation of an approximate similarity ranking. Experimental results demonstrate the effectiveness of the proposed framework and its applications.     

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。     

A modified medical image registration

Medical image registration is commonly used in clinical diagnosis, treatment, quality assurance, evaluation of curative efficacy and so on. In this paper, the edges of the original reference and floating images are detected by the B-spline gradient operator and then the binarization images are acquired. By computing the binarization image moments, the centroids are obtained. Also, according to the binarization image coordinates, the rotation angles of the reference and floating images are computed respectively, on the foundation of which the initial values for registering the images are produced. When searching the optimal geometric transformation parameters, the modified peak signal-to-noise ratio (MPSNR) is viewed as the similarity metric between the reference and floating images. At the same time, the simplex method is chosen as multi-parameter optimization one. The experimental results show that, this proposed method has a fairly simple implementation, a low computational load, a fast registration and good registration accuracy. It also can effectively avoid trapping in the local optimum and is adapted to both mono-modality and multi-modality image registrations. Also, the improved iterative closest point algorithm based on acquiring the initial values for registration from the least square method (LICP) is introduced. The experiments reveal that the measure acquiring the initial values for registration from image moments and the least square method (LSM) is feasible and resultful strategy.     

Iconic feature based nonrigid registration: the PASHA algorithm

In this paper, we first propose a new subdivision of the image information axis used for the classification of nonrigid registration algorithms. Namely, we introduce the notion of iconic feature based (IFB) algorithms, which lie between geometrical and standard intensity based algorithms for they use both an intensity similarity measure and a geometrical distance. Then we present a new registration energy for IFB registration that generalizes some of the existing techniques. We compare our algorithm with other registration approaches, and show the advantages of this energy. Besides, we also present a fast technique for the computation of local statistics between images, which turns out to be useful on pairs of images having a complex, nonstationary relationship between their intensities, as well as an hybrid regularization scheme mixing elastic and fluid components. The potential of the algorithm is finally demonstrated on a clinical application, namely deep brain stimulation of a Parkinsonian patient. Registration of pre- and immediate postoperative MR images allow to quantify the range of the deformation due to pneumocephalus over the entire brain, thus yielding to measurement of the deformation around the preoperatively computed stereotactic targets.     

频率域基于梯度预处理的互相关图像配准方法

传统的频率域图像配准方法有两种：基于互相关的图像配准方法和基于相位相关的图像配准方法。这两种方法都是通过确定逆傅立叶变换域最大峰值位置来获得配准信息。互相关图像配准方法与相位相关图像配准方法相比,主要存在两个缺点：一是峰值的跨度过大,二是有时存在多个峰值。本文对传统方法进行了改进,提出了频率域基于梯度预处理互相关的图像配准方法,该方法首先对待配准图像进行梯度预处理,然后对预处理后的图像用传统的互相关方法进行配准。实验表明,该方法很好地克服了上述传统方法的不足,并能获得精确的配准结果。     

基于兴趣边缘优化的壁画影像与激光扫描数据非刚性配准

将壁画影像与激光扫描数据配准,并进行定位和纠正在壁画的数字化保护中有非常重要的意义.本文以激光扫描数据强度信息为中介,提出了一种基于兴趣边缘优化的壁画影像与激光扫描数据的非刚性配准方法:由激光扫描数据生成强度影像,以壁画彩色影像的兴趣边缘和强度影像的梯度场作为配准基元,在影像刚性配准基础上,对每条兴趣边缘进行优化配准,然后以优化后边缘的特征点为控制点,构造影像之间的非刚性变换模型,完成壁画影像与激光扫描数据的配准.实验结果表明本方法在不同数据中都能获得较高的配准精度.     

Fair relay selection in decode-and-forward cooperation based on outage priority

The opportunistic cooperation schemes,where only the "best" relay is selected to forward the message,have been widely investigated recently for their good performance in terms of outage probability.However,the unfair selections of relays may cause unbalance power consumptions among relays,which reduces the lifetime of energy constrained networks.In this paper,we introduce a novel concept of outage priority based fairness(OPF),aiming at improving the selection fairness among relays appropriately without outage performance deterioration.Then,a cooperation scheme is proposed to meet this concept,and corresponding theoretical analysis is also provided.Afterward,based on OPF,the achievable upper bound of the fairness is derived,and an optimal cross-layer designed scheme is also provided to achieve the bound.Numerical simulations are carried out finally,which not only validate the theoretical analysis,but also show that taking advantages of the proposed schemes,the fairness among all relays,as well as the network lifetime,can be greatly improved without any loss of outage performance,especially in high SNR regime.     

基于干线对的红外与可见光最优图像配准算法

提出了一种基于干线对的红外与可见光图像配准算法.该算法分4步:首先分别从基准图像和待配准图像中提取干线对,即对图像中满足特定条件的直线进行配对;然后按照一些准则寻找这两幅图像中的干线对所有可能的匹配情况,并组成一个集合;接着从该集合中寻找这样一个子集,在保证每个干线对最多出现在它的一个元素中的前提下,使得该子集所有元素的相似性测度之和最大且由它确定的配准误差最小,该文采用分支定限法解决了这一优化问题;最后由最优子集中的所有元素得到同名像点集,运用仿射变换模型,实现图像的配准.大量实验表明,文中提出的方法对红外与可见光遥感图像之间的配准是有效的.     

基于复值小波分解的图象拼合

图象拼合是一种基于图象的场景编码方法，它被很多基于图象的绘制IBR（image based rendering）系统采用，用来建立复杂的虚拟场景表示（例如，360°球面和柱面全景图、环境映照及高分辨率图象等）.基于复值小波多分辨率分解(Complex Wavelet Multiresolution Decomposition)提出了一种有效的图象拼合方法，它能同时地、逐步求精地对图象进行匹配和整合.首先，采用复值小波变换不仅可以保证全局优化的结果，还能够满足图象整合的规模不变和平移不变性.其次，基于多分辨     

Image registration by compression

Image registration consists in finding the transformation that brings one image into the best possible spatial correspondence with another image. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that image registration can be formulated as a compression problem. Second, we demonstrate the good performance of the similarity metric, introduced by Li et al., in image registration. Two different approaches for the computation of this similarity metric are described: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images.     

2D/3D image registration on the GPU

We present a method that performs a rigid 2D/3D image registration efficiently on the Graphical Processing Unit (GPU). As one main contribution of this paper, we propose an efficient method for generating realistic DRRs that are visually similar to x-ray images. Therefore, we model some of the electronic post-processes of current x-ray C-arm-systems. As another main contribution, the GPU is used to compute eight intensity-based similarity measures between the DRR and the x-ray image in parallel. A combination of these eight similarity measures is used as a new similarity measure for the optimization. We evaluated the performance and the precision of our 2D/3D image registration algorithm using two phantom models. Compared to a CPU + GPU algorithm, which calculates the similarity measures on the CPU, our GPU algorithm is between three and six times faster. In contrast to single similarity measures, our new similarity measure achieved precise and robust registration results for both phantom models.     

Using photo-consistency to register 2D optical images of the humanface to a 3D surface model

The authors propose a novel method to register two or more optical images to a 3D surface model. The potential applications of such a registration method could be in medicine for example, in image guided interventions, surveillance and identification, industrial inspection, or telemanipulation in remote or hostile environments. Registration is performed by optimizing a similarity measure with respect to the transformation parameters. We propose a novel similarity measure based on "photo-consistency." For each surface point, the similarity measure computes how consistent the corresponding optical image information in each view is with a lighting model. The relative pose of the optical images must be known. We validate the system using data from an optical-based surface reconstruction system and surfaces derived from magnetic resonance (MR) images of the human face. We test the accuracy and robustness of the system with respect to the number of video images, video image noise, errors in surface location and area, and complexity of the matched surfaces. We demonstrate the algorithm working on 10 further optical-based reconstructions of the human head and skin surfaces derived from MR images of the heads of five volunteers. Matching four optical images to a surface model produced a 3D error of between 1.45 and 1.59 mm, at a success rate of 100 percent, where the initial misregistration was up to 16 mm or degrees from the registration position