An information-theoretic method for multimodality medical image registration

In this paper, an information-theoretic approach for multimodal image registration is presented. In the proposed approach, image registration is carried out by maximizing a Tsallis entropy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. This divergence measure achieves its maximum value when the conditional intensity probabilities of the transformed target image given the reference image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed approach in comparison to existing entropic image alignment techniques. The feasibility of the proposed algorithm is demonstrated on medical images from magnetic resonance imaging, computer tomography, and positron emission tomography.     

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.     

残差密集相对平均CGAN的脑部图像配准

目的 针对图像合成配准算法中鲁棒性差及合成图像特征信息不足导致配准精度不高的问题,提出了基于残差密集相对平均条件生成对抗网络（residual dense-relativistic average conditional generative adversarial network,RD-RaCGAN）的多模态脑部图像配准方法。方法 相对平均生成对抗网络中的相对平均鉴别器能够增强模型稳定性,条件生成对抗网络加入条件变量能提高生成数据质量,结合两种网络特点,利用残差密集块充分提取深层网络特征的能力,构建RD-RaCGAN合成模型。然后,待配准的参考CT（computed tomography）和浮动MR（magnetic resonance）图像通过已训练好的RD-RaCGAN合成模型双向合成对应的参考MR和浮动CT图像。采用区域自适应配准算法,从参考CT和浮动CT图像中选取骨骼信息的关键点,从浮动MR和参考MR图像中选取软组织信息的关键点,通过提取的关键点指导形变场的估计。从浮动CT图像到参考CT图像估计一个形变场。类似地,从浮动MR图像到参考MR图像估计一个形变场。另外,采用分层对称的思想进一步优化两个形变场,当两个形变场之间的差异达到最小时,将两个形变场融合得到最终的形变场,并将形变场作用于浮动图像完成配准。结果 实验结果表明,与其他6种图像合成方法相比,本文模型合成的目标图像在视觉效果和客观评价指标上均优于其他方法。对比Powell优化的MI（mutual information）法、ANTs-SyN（advanced normalization toolbox-symmetric normalization）、D.Demons（diffeomorphic demons）、Cue-Aware Net（cue-aware deep regression network）和I-SI（intensity and spatial information）的图像配准方法,归一化互信息分别提高了43.71%、12.87%、10.59%、0.47%、5.59%,均方根误差均值分别下降了39.80%、38.67%、15.68%、4.38%、2.61%。结论 本文提出的多模态脑部图像配准方法具有很强的鲁棒性,能够稳定、准确地完成图像配准任务。     

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.     

基于自适应分数阶的医学图像非刚性配准

现有的医学图像配准算法对于灰度均匀、弱边缘以及弱纹理图像易陷入局部最优从而导致配准精度低下、收敛速度缓慢. 分数阶主动Demons (Fractional active Demons, FAD)算法是解决该问题的有效方法, 并且适用于图像的非刚性配准. 但FAD中的最佳分数阶阶次是人工交互选取, 并且对整幅图像都是固定不变的. 为了解决该问题, 提出一种阶次自适应的主动Demons算法并将其应用到医学图像的非刚性配准中. 算法首先根据图像的局部特征建立分数阶阶次自适应的数学模型, 并逐像素计算最优阶次, 基于该阶次构造Riemann-Liouvill (R-L)分数阶微分动态模板; 然后将自适应R-L分数阶微分引入到Active Demons算法, 在一定程度上缓解了图像配准在弱边缘和弱纹理区域易陷入局部最优问题, 从而提高了配准精度. 通过在两个医学图像库上进行实验验证, 实验结果表明该方法可以处理灰度均匀、弱纹理和弱边缘的医学图像非刚性配准, 配准精度得到较大提升.     

Multimodal image registration system for image-guided orthopaedic surgery

We present a novel multimodality image registration system for spinal surgery. The system comprises a surface-based algorithm that performs computed tomography/magnetic resonance (CT/MR) rigid registration and MR image segmentation in an iterative manner. The segmentation/registration process progressively refines the result of MR image segmentation and CT/MR registration. For MR image segmentation, we propose a method based on the double-front level set that avoids boundary leakages, prevents interference from other objects in the image, and reduces computational time by constraining the search space. In order to reduce the registration error from the misclassification of the soft tissue surrounding the bone in MR images, we propose a weighted surface-based CT/MR registration scheme. The resultant weighted surface is registered to the segmented surface of the CT image. Contours are generated from the reconstructed CT surfaces for subsequent MR image segmentation. This process iterates till convergence. The registration method achieves accuracy comparable to conventional techniques while being significantly faster. Experimental results demonstrate the advantages of the proposed approach and its application to different anatomies.     

利用风格迁移和特征点的多模态图像配准算法

由于多模态遥感图像在光谱成份上存在巨大的差异,传统图像配准算法在该类图像的配准中正确率非常低.针对这一难题,提出了一种利用风格迁移和特征点的图像配准算法.首先,利用卷积神经网络对基准图像的风格特征以及待配准图像的内容特征进行抽取并重新组合,得到一幅与基准图像差异性较小的生成图像;其次,通过图像分割的方法分离出待配准图像...     

Estimating myocardial motion by 4D image warping

A method for spatio-temporally smooth and consistent estimation of cardiac motion from MR cine sequences is proposed. Myocardial motion is estimated within a four-dimensional (4D) registration framework, in which all three-dimensional (3D) images obtained at different cardiac phases are simultaneously registered. This facilitates spatio-temporally consistent estimation of motion as opposed to other registration-based algorithms which estimate the motion by sequentially registering one frame to another. To facilitate image matching, an attribute vector (AV) is constructed for each point in the image, and is intended to serve as a “morphological signature” of that point. The AV includes intensity, boundary, and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points to refine the registration. Experimental results on real data demonstrate good performance of the proposed method for cardiac image registration and motion estimation. The motion estimation is validated via comparisons with motion estimates obtained from MR images with myocardial tagging.     

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.     

基于局部重叠区域的无显著特征图像配准算法

针对沙漠、戈壁等特征不显著场景在配准过程中存在快速性、精确性等问题,提出一种基于局部重叠区域的特征不显著图像配准方法。首先利用图像标记对待配准图像进行预处理增强其特征,接着通过多相机三维投影对多幅待配准图像重叠区域进行预算,并采用图像掩膜和图像分割技术将重叠区域分割出来,最后对重叠区域使用ORB+GMS（Oriented Brief-Grid-based Motion Statistics for Fast）融合算法进行配准,完成多幅图像的配准工作。基于图像重叠区域的配准避免了无显著特征图像在进行整体配准时精确性低的缺点,并且由于是局部配准,相较于全局配准拥有更快的配准速度。对比传统配准方法和本文提出的改进配准方法,实验结果显示,本文提出的改进方法配准精度在传统配准方法的基础上提升了28%,同时,算法具有更高的鲁棒性和实时性。     

基于Radon变换与功率谱结合的心脏MR图像配准算法

针对心脏磁共振(MR)序列切片图像,设计了一种基于Radon变换和功率谱结合的图像配准算法。首先采用形态学边缘检测等图像预处理技术,提取出图像的边缘特征,并将其作为后续配准的输入;而后利用Radon变换和功率谱相结合的配准方法依次求出待配准图像的缩放、旋转和平移参数,利用这3个配准参数,即可通过配准变换得到配准结果。该方法解决了单纯利用Radon变换求解旋转参数易受图像空域噪声影响的问题,提高了配准的精度,同时大大减少了计算的花费。对100幅MR序列切片图像进行配准的实验表明,该方法能够稳定准确地实现MR序列图像的配准。     

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     

Efficient inhomogeneity compensation using fuzzy c-means clustering models

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based INU compensation and MR image segmentation algorithms. Experiments carried out using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.     

虚拟结肠镜中皱襞预匹配的结肠配准

目的 虚拟结肠镜是一种采用CT或者MRI图像重建出结肠3维结构,通过漫游虚拟结肠来检测结肠组织,一般用于早期结直肠癌筛查。结肠配准能够有效提高息肉检测的效率和精确度,但由于仰卧和俯卧位下的结肠图像形变太大,现有的配准方案中特征点的提取没有考虑到较多特殊情况,因此需要寻找一个新的配准方案完成完整的结肠配准。方法 提出了一种新的结肠图像配准方法,能够完成不同体位获取的虚拟结肠图像之间的配准。首先提取可以反映结肠结构信息的皱襞特征,用模板匹配和特征匹配方法找出两幅结肠中匹配的皱襞对。然后将匹配对的中心点作为标记点,做基于标记点的非刚性粗配准,最后将两幅图做B样条配准完成细配准。这种方法能够将结肠内部较大的形变先矫正,使得两幅图之间的形变缩小到一定范围,然后利用传统配准方法能够完成配准。结果 在5套数据中,找到能够成功匹配的皱襞区域数量占所有分割出的皱襞区域总数量的62%左右,匹配错误率为4.7%左右。完成皱襞粗配准后,结肠形变明显趋于一致,灰度值相对误差减小,最终完成了结肠配准。结论 先进行皱襞匹配再做基于匹配好的皱襞的映射关系做结肠配准,能够将存在较大形变的两套结肠匹配起来。在之后的工作中需要量化特征点选取对配准结果的影响,同时在做配准评估时,单纯采用灰度差值不能很好完成评估,因为灰度特征只能一定程度反映整体差异,不能很好体现结构差异,需要添加其他评估标准辅助配准评估。     

心肌灌注核磁共振图像的非刚性配准

心肌灌注核磁共振图像中心脏的位置和形状会随着病人的呼吸和心跳而发生改变.同时,灌注图像的灰度也会随着造影剂在心腔中的流动而不断变化.这使得传统单纯依靠互信息、互相关等信息的配准算法很难在心肌灌注核磁共振图像中得到应用.使用马尔可夫随机场(M RF)模型对图像进行非刚性配准.在马尔可夫能量函数中对图像块进行归一化,以消除两幅图像的灰度差异对配准的影响.为了消除序列图像配准中产生的积累误差,对心肌灌注MR序列中的每幅图像计算对应的伪真实(PGT)图像,并将这幅伪真实图像作为配准模板.通过将心肌灌注MR序列中的每幅图像与其对应的伪真实图像进行配准,避免了序列图像配准中积累误差的产生.实验结果表明,该方法有效地矫正了心肌灌注MR图像中心脏的位移和弹性形变.     

基于改进平衡优化器的医学2D/3D图像快速配准算法

医学三维图像(如CT、MRI等)和二维图像(如X光)的配准技术已经被广泛应用于临床诊断和手术规划中. 医学图像配准的实质为使用优化算法寻找某种空间变换, 使两张图像在空间以及结构上对齐. 配准过程中往往由于优化算法寻优精度不高、易陷入局部极值的问题导致配准质量低. 针对此问题, 提出一种改进的平衡优化器算法(improved equilibrium optimizer based on Logistic-Tent chaos map and Levy flight, LTEO), 首先针对种群初始化容易分布不均匀, 且随机性太高的问题, 引入Logistic-Tent混沌映射对种群进行初始化, 提高种群多样性, 使它们尽可能地分布于搜索空间内; 对迭代函数进行更新, 使得优化算法更注重全局范围的搜索, 提高算法收敛速度并利于找到全局最优解; 引入Levy飞行策略对停滞粒子进行扰动, 防止算法陷入局部极值. 最后将改进的平衡优化器算法用于2D/3D医学图像配准任务, 并对配准过程中数据的频繁传输进行优化, 降低配准耗时. 通过基准函数测试和临床配准实验对算法进行验证, 改进后的平衡优化器可有效提高寻优精度和稳定性, 并提高医学图像配准的质量.     

基于Demon算法的乳房核磁共振图像配准

为了提高乳房动态核磁共振成像中不同时间点序列图像的配准速度,提出了将快速的Demon非刚性配准算法与图像强度校正相结合的乳房图像配准模型。Demon算法本身基于图像强度变化计算变形参数,因此不适于强度动态增强的图像配准。首先根据乳房组织的信号增强模型应用多项式理论校正待配准图像的强度,再应用Demon算法进行几何变形配准。实验表明该算法在精度上远优于直接使用Demon算法,在与自由形变配准算法具有几乎相同配准精度的前提下,明显提高了配准速度。     

Unsupervised deformable image registration network for 3D medical images

Image registration aims to establish an active correspondence between a pair of images. Such correspondence is critical for many significant applications, such as image fusion, tumor growth monitoring, and atlas generation. In this study, we propose an unsupervised deformable image registration network (UDIR-Net) for 3D medical images. The proposed UDIR-Net is designed in an encoder-decoder architecture and directly estimates the complex deformation field between input pairwise images without any supervised information. In particular, we recalibrate the feature slice of each feature map that is propagated between the encoder and the decoder in accordance with the importance of each feature slice and the correlation between feature slices. This method enhances the representational power of feature maps. To achieve efficient and robust training, we design a novel hierarchical loss function that evaluates multiscale similarity loss between registered image pairs. The proposed UDIR-Net is tested on different public magnetic resonance image datasets of the human brain. Experimental results show that UDIR-Net exhibits competitive performance against several state-of-the-art methods.

     

基于有偏场的适配模糊聚类分割算法

近年来提出了许多监督和非监督模式识别技术用于磁共振图象的组织分类和定量分析。但是，这些方法的精度受到图象灰度不均匀性的严重影响。对于那些受灰度不均匀性影响的图象，如磁共振图象（MRI），提出了一种新的基于有偏场适配模糊聚类算法（BAFCM）来产生它们的分割结果。该算法通过修改C－means模糊算法中的目标方程，引入了描述灰度不均匀性的增益场，通过将增益场经为有偏场的计算，避免了AFCM中为保证增益场平滑缓慢变化引入的第一阶和第二阶正则项空间改变的二阶差分方程的复杂计算。其不仅对图象的灰度不均匀性进行了校正，而且实现了脑组织图象的快速全自动分割。     

Junction detection for linear structures based on Hessian,correlation and shape information

Junctions have been demonstrated to be important features in many visual tasks such as image registration, matching, and segmentation, as they can provide reliable local information. This paper presents a method for detecting junctions in 2D images with linear structures as well as providing the number of branches and branch orientations. The candidate junction points are selected through a new measurement which combines Hessian information and correlation matrix. Then the locations of the junction centers are refined and the branches of the junctions are found using the intensity information of a stick-shaped window at a number of orientations and the correlation value between the intensity of a local region and a Gaussian-shaped multi-scale stick template. The multi-scale template is used here to detect the structures with various widths. We present the results of our algorithm on images of different types and compare our algorithm with three other methods. The results have shown that the proposed approach can detect junctions more accurately.