基于SURF特征与边缘信息的图像配准

目的:针对特征图像配准方法速度快、效率高,而医学图像具有结构信息不明显、变形复杂等特点,该类方法常常失效,提出一种结合SURF特征与图像边缘信息的配准方案.方法:首先使用SURF法检测图像的特征点,采用角度法判断特征点是否匹配错误,并将误匹配的特征点删除;然后,将所得特征点对与图像的Canny边缘相结合,形成一个新的特...     

一种病变眼底图像的配准方法

对于具有病变的眼底图像,血管结构不够清晰,采用基于血管分割和血管分支点、交叉点等眼底图像配准方法具有一定的局限性。为了解决这个问题,提出一种基于不变特征的眼底图像配准方法。提取眼底图像的尺度不变特征(SIFT)作为特征点,提出双边或的Best-Bin-First(BBF)算法进行特征点匹配,并根据特征点具有旋转不变性的方向特征和空间斜率及空间距离等几何特性的一致性检测去除误匹配,精化匹配特征,利用得到的匹配特征进行M估计得到眼底图像的变换关系。通过对不同程度病变的眼底图像数据进行配准实验,观察配准结果,对比匹配特征的正确匹配数量和分析衡量配准精度的均方根误差。结果表明,该方法实现了良好的细节对齐,保留了足够的正确匹配对,对实验的病变眼底图像配准成功后的均方根误差均小于1,且浮动于0.5左右,验证了方法的精确性和有效性。     

利用边缘和表面特征实现脑图像的自动配准

本文中我们使用基于CT、MR和PET图像等值特征表面的配准算法对多模医学图像进行了配准研究，在CT、MR和PET的原始图像中提取等值特征表面，进行图像的几何对准，并对结果进行初步评估，同时对该算法的稳健性，搜索最近点策略和采样策略进行了研究，结果表明；这种方法能够达到亚像素级的配准精度，是一种稳健、高精度、全自动的配准方法。     

一种适用于肝脏CT图像配准改进的尺度不变特征变换算法

针对肝脏CT图像特点,在传统的尺度不变特征变换(scale-invariant feature transform,SIFT)算法基础上,结合K-means聚类算法,提出了一种改进的特征点匹配算法。该算法通过聚类SIFT特征点坐标,将配准图像分为4个区域,特征点分块配准。与原算法相比,该算法增加了特征点匹配数量,有效隔离了特征点跨区域的错误匹配,时间复杂度也得到了一定的降低。该算法还减少了肝脏CT图像配准中错误匹配对配准结果的影响,提升了肝脏CT图像的配准精度。     

手术导航中空间坐标系配准与注册研究

目的手术导航精度对手术的效果有着至关重要的影响,笔者研究了影响导航精度的因素,包括标志点的选取方法和注册算法。标志点的选取影响配准中两个空间相同目标点的选取,而注册算法影响利用这两组点进行空间配准的精度。方法首先分别阐述了在注册过程中基于点匹配和基于点云的迭代最近点(ICP)匹配点选取方法和注册算法,随后对它们的特点与原理进行分析,比较了其优缺点。提出一种基于点匹配的系统注册方法结合四元数坐标系配准方法,并通过实验验证该方法在将手术空间和图像空间配准时符合手术导航精度要求。同时,分析了这种系统注册方法将来存在的问题和研究方向。结果利用光学导航仪追踪注册模具上的反光球,得到模具内乒乓球的球心坐标,而后在CT图像中获取乒乓球的球心坐标,在Visual Studio中验证两组点的配准,实验结果表明误差在科学可接受范围内。结论这种新的系统注册模具可以用于手术导航注册,结合四元数法精度是符合手术导航精度的。     

利用边缘和表面特征实现脑图像CT-MR的自动配准

应用基于CT和MR图像等值特征表面的配准算法对多模医学图像进行了配准研究.在CT、MR图像中提取等值特征表面,进行图像的几何对准,并对结果进行初步评估,同时对该算法的稳健性,搜索最近点策略和插值策略进行了研究.结果表明:这种方法能够达到亚象素级的配准精度,是一种稳健、高精度、全自动的配准方法.     

一种基于感兴趣点旋转不变性特征的图像配准新方法

目的:图像配准是图像处理领域重要的研究方向,是图像融合、图像重建和图像分析等研究的基础。在图像配准的主要方法中,基于图像特征的配准方法和基于图像灰度的配准方法各有优缺点,通过结合这两种方法的优点,我们提出了一种基于感兴趣点的旋转不变性特征图像配准的新方法。方法:首先利用Harris角点检测技术,提取模板图像和目标图像的感兴趣点。然后把感兴趣点的旋转不变形特征和灰度值组成图像的特征描述向量,并提出新的代价函数。最后采用分级优化的策略优化代价函数,在配准初期,采用显著的特征点进行配准,以保证配准的速度与鲁棒性,随后通过逐步增加特征点的数量,则保证了配准的精度。结果:为显示本文方法的优越性,实验利用本文方法和基于互信息的B样条方法分别对标准测试图像进行配准,实验结果表明,本文方法较基于互信息的B样条方法在配准精度上有明显提高。结论:本文方法在保持配准鲁棒性的前提下,获得了较高的配准精度。     

基于关键点的超声图像与磁共振图像多分辨率离散优化配准方法

脑肿瘤手术规划及术中，术前磁共振（MR）图像与术中超声（US）图像的配准甚为关键。考虑到两种模态图像具有不同密度范围及分辨率，且超声图像存在较多的斑点噪声干扰，采用一种基于局部邻域信息的自相似性上下文（SSC）描述子定义图像之间的相似性测度。将超声图像作为参考，使用三维微分运算提取其中角点作为关键点，并采用密集位移采样离散优化算法实施配准。整个配准过程分为仿射配准和弹性配准两个阶段，在仿射配准阶段，对图像进行多分辨率分解，在弹性配准阶段，采取最小卷积和均值场推理策略对关键点的位移向量进行正则化处理。对22名患者的术前MR和术中US图像进行配准实验，仿射配准后的误差为（1.57±0.30）mm，每对图像配准平均耗时1.36 s；弹性配准后的误差为（1.40±0.28）mm，平均用时1.53 s。实验结果证明本文采用的方法具有良好的配准精度和速度。     

一种基于小波变换的配准特征点自动标记算法

以颅脑CT图像为研究对象,提出了一种基于小波变换的自动标记非刚性配准所需对应特征点的算法.这种算法充分考虑了颅脑CT图像的像素点及其临域的纹理特征,通过进行小波变换建立对应于每个像素点的多分辨率小波特征向量,并以小波特征向量间的差异作为判别依据,在目标图像中标记非刚性配准所需的对应特征点.一系列的实验结果表明,这种基于小波变换的算法能够准确地在目标图像中标记出配准所需的对应特征点,可以作为基于特征的非刚性配准对应特征点自动标记的参量之一.     

基于伪超声和互信息的磁共振与超声图像配准

手术中超声图像与术前磁共振图像的配准在手术导航系统中非常重要。我们利用磁共振和超声成像原理,提出了基于伪超声和互信息,并结合多分辨率技术与Powell优化算法对两种模态图像进行配准的方法,该方法可以有效降低寻优过程中陷入局部极值收敛的概率,提高两种模态图像的配准精度。实验结果表明,我们提出的基于伪超声和互信息的配准方法比目前手术导航系统中普遍采用的标记点方法具有更高的配准精度。     

基于自由变形法的多模态医学图像的配准与融合

本研究提出了一种自动识别颈部PET-CT图像特征点的算法，它应用自由变形（FFD）方法以CT图像的特征点为参考使PET图像产生变形，再结合最大互信息法对颈部PET与CT图像进行非刚体配准，最后用改进的小波图像融合法把两者进行融合得出视觉效果比较理想的融合图像。经实际计算得出的变形PET图像与对应CT图像的互信息量大于原始PET图像，并且最后用改进的小波图像融合法得出的融合图像的信息量比一般小波融合大，由此证明本研究所用方法是有效的。     

基于曲线描述子的手指静脉识别

手指静脉识别因为具有高防伪性、唯一性、稳定性和活体检测等优点,成为身份识别领域的研究热点。目前大多数基于指静脉结构特征的识别算法仅考虑到了细节点特征,却忽略了静脉网络结构的曲线特征,造成一部分结构信息的丢失,影响识别结果。针对上述问题,提出一种基于曲线描述子的手指静脉识别算法。首先,提取出指静脉的骨架结构,检测静脉交叉点和端点,并利用交叉点和端点将静脉骨架分割为若干条曲线段;其次,通过交叉点和曲线段的相对位置及形状特征提出曲线弧描述子和交叉弧描述子,并提取指静脉的结构特征矩阵;最后,根据提出的加权距离式计算匹配交叉弧对进行图像匹配。对实验室采集的来自56名志愿者的840张手指静脉图像进行算法实验,结果表明,传统的局部二值模式(LBP)、局部三值模式(LTP)和加速稳健特征(SURF)算法的等错误率分别为4.47%、3.99%和6.08%,而本方法的等错误率仅为1.63%。所提出方法在指静脉识别中具有一定的普适性和应用前景。     

Robust feature-based registration using a Gaussian-weighted distance map and brain feature points for brain PET/CT images

Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method.     

Robust image registration for functional magnetic resonance imaging of the brain

Motion-related artifacts are still a major problem in data analysis of functional magnetic resonance imaging (FMRI) studies of brain activation. However, the traditional image registration algorithm is prone to inaccuracy when there are residual variations owing to counting statistics, partial volume effects or biological variation. In particular, susceptibility artifacts usually result in remarkable signal intensity variance, and they can mislead the estimation of motion parameters. In this study, Two robust estimation algorithms for the registration of FMRI images are described. The first estimation algorithm was based on the Newton method and used Tukey's biweight objective function. The second estimation algorithm was based on the Levenberg-Marquardt technique and used a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. The proposed registration methods consisted of the following steps: fast segmentation of the brain region from noisy background as a preprocessing step; pre-registration of the volume centroids to provide a good initial estimation; and two robust estimation algorithms and a voxel sampling technique to find the affine transformation parameters. The accuracy of the algorithms was within 0.5 mm in translation and within 0.5° in rotation. For the FMRI data sets, the performance of the algorithms was visually compared with the AIR 2.0 software, which is a software for image registration, using colour-coded statistical mapping by the Kolmogorov-Smirov method. Experimental results showed, that the algorithms provided significant improvement in correcting motion-related artifacts and can enhance the detection of real brain activation.     

Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans

We evaluated the image registration accuracy achieved using two deformable registration algorithms when radiation-induced normal tissue changes were present between serial computed tomography (CT) scans. Two thoracic CT scans were collected for each of 24 patients who underwent radiation therapy (RT) treatment for lung cancer, eight of whom experienced radiologically evident normal tissue damage between pre- and post-RT scan acquisition. For each patient, 100 landmark point pairs were manually placed in anatomically corresponding locations between each pre- and post-RT scan. Each post-RT scan was then registered to the pre-RT scan using (1) the Plastimatch demons algorithm and (2) the Fraunhofer MEVIS algorithm. The registration accuracy for each scan pair was evaluated by comparing the distance between landmark points that were manually placed in the post-RT scans and points that were automatically mapped from pre- to post-RT scans using the displacement vector fields output by the two registration algorithms. For both algorithms, the registration accuracy was significantly decreased when normal tissue damage was present in the post-RT scan. Using the Plastimatch algorithm, registration accuracy was 2.4 mm, on average, in the absence of radiation-induced damage and 4.6 mm, on average, in the presence of damage. When the Fraunhofer MEVIS algorithm was instead used, registration errors decreased to 1.3 mm, on average, in the absence of damage and 2.5 mm, on average, when damage was present. This work demonstrated that the presence of lung tissue changes introduced following RT treatment for lung cancer can significantly decrease the registration accuracy achieved using deformable registration.     

A comparative study of surface- and volume-based techniques for the automatic registration between CT and SPECT brain images

Image registration of multimodality images is an essential task in numerous applications in three-dimensional medical image processing. Medical diagnosis can benefit from the complementary information in different modality images. Surface-based registration techniques, while still widely used, were succeeded by volume-based registration algorithms that appear to be theoretically advantageous in terms of reliability and accuracy. Several applications of such algorithms for the registration of CT-MRI, CT-PET, MRI-PET, and SPECT-MRI images have emerged in the literature, using local optimization techniques for the matching of images. Our purpose in this work is the development of automatic techniques for the registration of real CT and SPECT images, based on either surface- or volume-based algorithms. Optimization is achieved using genetic algorithms that are known for their robustness. The two techniques are compared against a well-established method, the Iterative Closest Point-ICP. The correlation coefficient was employed as an independent measure of spatial match, to produce unbiased results. The repeated measures ANOVA indicates the significant impact of the choice of registration method on the magnitude of the correlation (F = 4.968, p = 0.0396). The volume-based method achieves an average correlation coefficient value of 0.454 with a standard deviation of 0.0395, as opposed to an average of 0.380 with a standard deviation of 0.0603 achieved by the surface-based method and an average of 0.396 with a standard deviation equal to 0.0353 achieved by ICP. The volume-based technique performs significantly better compared to both ICP (p<0.05, Neuman Keuls test) and the surface-based technique (p<0.05, Neuman-Keuls test). Surface-based registration and ICP do not differ significantly in performance.     

基于互信息的多分辨率三维脑图像配准方法

在3D多模医学图像的配准方法中，最大互信息法精度高，鲁棒性强，使用范围广，本文将归一化互信息作为相似性测度，采用不同的采样范围和采样子集，使用Powell多参数优化法和Brent一维搜索算法对3DCT，MR和PET脑图像进行了刚体配准，为了加快配准速度，使用了多分辨的金字塔方法，对PET图像采用基于坐标的阈值选取方法对图像进行分割预算法，消除了大部分放射状背景伪影，美国万德贝尔大学对结果进行的评估证明配准精度可达亚体元级。     

3D Elastic Registration of Ultrasound Images Based on Skeleton Feature

INTRODUCTION The development of3D imaging has attracted great attention in the field of med-ical imaging by recent years.A majority of investigations in ultrasound imaging sys-tem have also focused on3D ultrasound image reconstruct system.All those recon-struct system based on recombination of2D images has a same condition that spatialposition of object being scanned remains unchanged as time passed by.Only in thisway,3D figure of human’s organ can be reconstructed by2D images obtained…