Multi-modality liver image registration based on multilevel B-splines free-form deformation and L-BFGS optimal algorithm

A new coarse-to-fine strategy was proposed for nonrigid registration of computed tomography(CT) and magnetic resonance(MR) images of a liver.This hierarchical framework consisted of an affine transformation and a B-splines free-form deformation(FFD).The affine transformation performed a rough registration targeting the mismatch between the CT and MR images.The B-splines FFD transformation performed a finer registration by correcting local motion deformation.In the registration algorithm,the normalized mutual information(NMI) was used as similarity measure,and the limited memory Broyden-Fletcher- Goldfarb-Shannon(L-BFGS) optimization method was applied for optimization process.The algorithm was applied to the fully automated registration of liver CT and MR images in three subjects.The results demonstrate that the proposed method not only significantly improves the registration accuracy but also reduces the running time,which is effective and efficient for nonrigid registration.

A method based on mutual information and gradient information for medical image registration

Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.

Novel registration algorithm for 3-D images captured from multiple views of object surface

A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.

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

A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time.

A survey on image and video stitching

Image/video stitching is a technology for solving the field of view(FOV)limitation of images/videos.It stitches multiple overlapping images/videos to generate a wide-FOV image/video,and has been used in various fields such as sports broadcasting,video surveillance,street view,and entertainment.This survey reviews image/video stitching algorithms,with a particular focus on those developed in recent years.Image stitching first calculates the corresponding relationships between multiple overlapping images,deforms and aligns the matched images,and then blends the aligned images to generate a wide-FOV image.A seamless method is always adopted to eliminate such potential flaws as ghosting and blurring caused by parallax or objects moving across the overlapping regions.Video stitching is the further extension of image stitching.It usually stitches selected frames of original videos to generate a stitching template by performing image stitching algorithms,and the subsequent frames can then be stitched according to the template.Video stitching is more complicated with moving objects or violent camera movement,because these factors introduce jitter,shakiness,ghosting,and blurring.Foreground detection technique is usually combined into stitching to eliminate ghosting and blurring,while video stabilization algorithms are adopted to solve the jitter and shakiness.This paper further discusses panoramic stitching as a special-extension of image/video stitching.Panoramic stitching is currently the most widely used application in stitching.This survey reviews the latest image/video stitching methods,and introduces the fundamental principles/advantages/weaknesses of image/video stitching algorithms.Image/video stitching faces long-term challenges such as wide baseline,large parallax,and low-texture problem in the overlapping region.New technologies may present new opportunities to address these issues,such as deep learning-based semantic correspondence,and 3D image stitching.Finally,this survey discusses the challenges of image/video stitching and proposes potential solutions.

Robust multi-sensor image matching based on normalized self-similarity region descriptor

Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images.However,it remains a challenging task due to significant non-linear radiometric,geometric differences,and noise across different sensors.To improve the performance of heterologous image matching,this paper proposes a normalized self-similarity region descriptor to extract consistent structural information.We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images.Then,a linear normalization approach is used to form Modality Independent Region Descriptor(MIRD),which can effectively distinguish structural features such as points,lines,corners,and flat between multi-modal images.To further improve the matching accuracy,the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors.The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods.MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation,effectively applied to various multi-modal image matching.

Transformers in medical image analysis

Transformers have dominated the field of natural language processing and have recently made an impact in the area of computer vision.In the field of medical image analysis,transformers have also been successfully used in to full-stack clinical applications,including image synthesis/reconstruction,registration,segmentation,detection,and diagnosis.This paper aimed to promote awareness of the applications of transformers in medical image analysis.Specifically,we first provided an overview of the core concepts of the attention mechanism built into transformers and other basic components.Second,we reviewed various transformer architectures tailored for medical image applications and discuss their limitations.Within this review,we investigated key challenges including the use of transformers in different learning paradigms,improving model efficiency,and coupling with other techniques.We hope this review would provide a comprehensive picture of transformers to readers with an interest in medical image analysis.

ITK Metric在卷烟小包装图像配准中的性能比较

为了验证医学图像处理框架ITK在卷烟小包装图像配准中的可用性及其性能,通过分析ITK配准框架的原理及配准流程,采用固定图像变换算法、线性插值算法和优化算法,变换相似度测度算法的方式,比较ITK中不同的相似度测度算法在针对中华硬盒卷烟小包装的图像配准中的效率,最后通过大量的实验,将此框架用于不同卷烟小包装的图像配准中,验证了ITK在非医学领域的可用性,得出一种对不同卷烟小包装的图像配准具有一定普适性的配准流程。

基于Harris多尺度角点检测的图像配准新算法

为改进角点检测算子的检测性能,提高基于角点的图像配准算法的配准精度,把多分辨分析的思想引入到经典的Harris角点检测中,构造了基于小波变换的灰度强度变化公式,并得到了具有尺度变换特性的自相关矩阵,从而构建了一种新的Harris多尺度角点检测算法。这样,使得新的角点检测可以在不同的尺度下获取角点,并克服了单一尺度的Harris角点检测可能存在的角点信息丢失、位置偏移和易受噪而提取出伪角点等问题。然后根据角度直方图得到的旋转角度,和提取的以角点为中心的特征子图,定义了角点点对的对齐度。最后,运用最大化对齐度准则来精确地确定角点匹配点对。实验表明,该配准算法具有精确性、有效性和抗噪性,实现了良好的配准效果。

基于对齐度的电力设备红外与可见光的图像配准

提出了一种全新的电力设备红外与可见光图像的配准算法,该方法基于图像的小波变换与对齐度最大化完成图像的配准过程。首先进行图像预处理,分别对红外图像取其负像以及对可见光图像取其灰度图像,从而提升两者的相似性特征;然后利用小波变换提取出图像的边缘及特征点,最后利用对齐度准则计算所有特征点对之间的对齐度,得到匹配点对。实验结果证明了此方法的有效性、精确性和适用性。

基于最大化对齐度的多模态图像自动配准

该文讨论图像配准的广义定义并提出一种新的图像配准准则—对齐度。对齐度能够正确地反映两幅不同灰度属性的图像是否配准,并且将图像配准归结为对齐度的最大化。新准则可以用来配准多模态图像包括灰度属性差异很大的图像。该文应用多分辨率方法和Powell方向族搜索法来最大化对齐度。因此,用该方法可以自动地处理图像配准问题,不需要定义地标点或特征点。多模态医学图像和遥感图像的自动配准的例子说明了该文方法的有效性。

医学图像配准技术进展

医学图像配准对提高临床诊断治疗、病情监测、外科手术水平等有积极作用。以一个医学图像配准配框架为主线,综述了配准框架各模块所涉及的经典算法、新技术,并对相关性能进行了分析。此外,还涉及医学图像配准开发平台、测试数据库及评估标准等。全面地总结了医学图像配准技术的最新进展。

ICP框架下基于表面间平均体积测度的深度像配准

提出一种利用深度像重叠区域间的空间体积作为误差度量的精确配准算法.通过寻找2幅深度像重叠区域内的有效三角形对,并将这些三角形对所夹的三维空间作为误差测度来指导深度像的配准;然后将对应三角形的质心作为对应点对,估计出新的空间位置转换关系.实验结果表明:该算法具有较高的配准精度,收敛速度快并且具有一定的抗噪声能力.

最大对齐度算法在多模态图像配准中正负像无关性分析

基于对齐度的配准是一种重要的多模态图像配准方法,其中的图像预处理是关键。在对红外热像仪图像和可见光图像进行配准的过程中,首先进行图像预处理,其中一个环节是将红外热像仪图像取负像,这样可使之与相应的可见光灰度图像比较相近。实验结果表明,对齐度配准算法与正负像无关,即可见光图像与相应的红外热像仪图像正像和负像的对齐度配准结果相同,理论证明也表明该结论的正确性。这一规律对于提高最大对齐度配准算法的计算效率具有重要意义,同时也具有一定的理论意义。

基于PDE的自适应各向异性图像配准方法研究

针对传统光流场配准模型会造成图像模糊和细节丢失的问题,提出了一种基于偏微分方程的自适应各向异性配准模型。新模型将具有自适应性的扩散滤波方法引入图像配准,定义具有图像结构保持能力的各向异性扩散函数作为模型的正则项;数据项采用作用于亮度常量假设的非二次惩罚函数以增加模型的稳健性。实验结果表明,新模型能够有效保持图像特征,实现对大脑等复杂图像的有效配准。

基于小波多尺度积的图像配准方法

由于基于特征的图像配准方法,对提取图像特征的鲁棒性和精确性都有很高的要求.本文提出了一种基于小波多尺度积的图像配准方法。该方法利用小波多尺度积提取边缘图像和特征点,同时保存特征点的方向信息,具有抗噪性强和边缘定位精确等优点;然后利用特征点的方向信息,定义角度直方图来得到图像之间的精确旋转角度;最后可以选择多种匹配准则来确定匹配点对。实验结果表明,本文提出的方法具有精确性、抗噪性、有效性和鲁棒性等优点。

基于色彩校正与分层块匹配的图像配准算法

针对当前图像匹配算法难以兼顾匹配精度和效率,易受模糊、噪声和色彩不一致影响等问题,提出色彩校正耦合分层块匹配优化的彩色图像配准算法。计算RGB图像每个色彩通道强度的平均值和标准差,建立色彩映射方程,对色彩不一致进行补偿,完成色彩校正;基于广义高斯分布金字塔RGB强度的差分和,确定高斯金字塔层数,进行特征检测,计算特征位置;进行初始块匹配,利用变化的单位比例因子对计算成本优化,利用抛物线拟合技术处理金字塔的每个通道,寻找其亚像素精度的最优匹配。仿真结果表明,该算法能够提高计算效率和精度,在色彩不一致、模糊和噪声图像上取得了良好配准结果,具有较强抗干扰能力与较高的鲁棒性。

基于混合遗传算法和表面间平均体积度量的深度像匹配

提出了一种利用改进的遗传算法和表面间平均体积测度进行深度像匹配的方法.与现有基于距离的误差评判不同,该方法通过衡量深度像重叠区域内每个三角形所对应三维空间的大小来指导深度像的配准.另外,遗传算法的使用避免了困扰ICP算法的初值选取问题,退火选择、最优个体迁移以及动态的空间退化都保证文中所提出的混合遗传算法比传统的遗传算法具有更快的收敛速度,能够有效地完成深度像的精确匹配.实验结果表明该算法具有较高的配准精度,收敛速度快而且抗噪声能力强.

基于外部基准框架的SPECT与MRI/CT的刚性配准及融合

设计了一种简便、有效的基于外部基准框架的SPECT(单光子发射型计算机断层 )与MRI/CT的刚性配准及融合的方法。在对图像进行一值化分割的基础上 ,运用侵蚀、扩张等图像处理算法确定图像中用于配准的相应点的坐标 ,然后利用这些对应点最小化代价函数并获得一个最佳刚性变换 ,实现图像的精确配准及融合显示。通过对作为外部基准框架的N型支架的设计及成像过程中对其与病人相对位置的严格限制 ,最大程度地减少成像中的误差。

基于度量矩阵特征值的图像空间关系描述

构造了在平移缩放旋转下保持不变的基于度量矩阵特征值的图像空间关系描述子。理论计算和仿真实验表明这种描述与人的主观视觉心理相一致,在平移缩放旋转下以及小噪声下具有较强的鲁棒性,具有较好的类别可分离性。与传统的空间关系描述方法如几何法和图匹配法相比,可以更好地描述图像空间关系,同时计算量有所减小。用于图像识别、图像检索、图像配准取得了较好效果。