3D Elastic Registration of Ultrasound Images Based on Skeleton Feature

In order to eliminate displacement and elastic deformation between images of adjacent frames in course of 3D ultrasonic image reconstruction, elastic registration based on skeleton feature was adopt in this paper. A new automatically skeleton tracking extract algorithm is presented, which can extract connected skeleton to express figure feature. Feature points of connected skeleton are extracted automatically by accounting topical curvature extreme points several times. Initial registration is processed according to barycenter of skeleton. Whereafter, elastic registration based on radial basis function are processed according to feature points of skeleton. Result of example demonstrate that according to traditional rigid registration, elastic registration based on skeleton feature retain natural difference in shape for organr s different part, and eliminate slight elastic deformation between frames caused by image obtained process simultaneously. This algorithm has a high practical value for image registration in course of 3D ultrasound image reconstruction.

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.

Non-Rigid Image Registration Algorithm Based on B-Splines Approximation

An intensity-based non-rigid registration algorithm is discussed, which uses Gaussian smoothing to constrain the transformation to be smooth, and thus preserves the topology of images. In view of the insufficiency of the uniform Gaussian filtering of the deformation field, an automatic and accurate non-rigid image registration method based on B-splines approximation is proposed. The regularization strategy is adopted by using multi-level B-splines approximation to regularize the displacement fields in a coarse-to-fine manner. Moreover, it assigns the different weights to the estimated displacements according to their reliabilities. In this way, the level of regularity can be adapted locally. Experiments were performed on both synthetic and real medical images of brain, and the results show that the proposed method improves the registration accuracy and robustness.

Non-rigid registration of medical images based on S^(1/2)(Δ_(mn)^((2)))non-tensor product Bspline

In this study,a non-tensor product B-spline algorithm is applied to the search space of the registration process,and a new method of image non-rigid registration is proposed.The tensor product B-spline is a function defined in the two directions of x and y,while the non-tensor product B-spline S^(1/2)(Δ_(mn)^((2)))is defined in four directions on the 2-type triangulation.For certain problems,using non-tensor product B-splines to describe the non-rigid deformation of an image can more accurately extract the four-directional information of the image,thereby describing the global or local non-rigid deformation of the image in more directions.Indeed,it provides a method to solve the problem of image deformation in multiple directions.In addition,the region of interest of medical images is irregular,and usually no value exists on the boundary triangle.The value of the basis function of the non-tensor product B-spline on the boundary triangle is only 0.The algorithm process is optimized.The algorithm performs completely automatic non-rigid registration of computed tomography and magnetic resonance imaging images of patients.In particular,this study compares the performance of the proposed algorithm with the tensor product B-spline registration algorithm.The results elucidate that the proposed algorithm clearly improves the accuracy.

Rapid and robust medical image elastic registration using mean shift algorithm

In landmark-based image registration, estimating the landmark correspondence plays an important role. In this letter, a novel landmark correspondence estimation technique using mean shift algorithm is proposed. Image corner points are detected as landmarks and mean shift iterations are adopted to find the most probable corresponding point positions in two images. Mutual information between intensity of two local regions is computed to eliminate mis-matching points to improve the stability of corresponding estimation correspondence landmarks is exact. The proposed experiments of various mono-modal medical images. Multi-level estimation （MLE） technique is proposed Experiments show that the precision in location of technique is shown to be feasible and rapid in the

Multilevel B-Spline Repulsive Energy in Nanomodeling of Graphenes

Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using standard parametrizations. The investigation is based on Density Functional Theory and Tight Binding simulations. Our objective is not only to capture the values of the repulsive terms but also to efficiently reproduce the elastic properties and the forces. The elasticity values determine the rigidity of a material when some traction or load is applied on it. The pair-potential is based on an exponential term corrected by B-spline terms. In order to accelerate the computations, one uses a hierarchical optimization for the B-splines on different levels. Carbon graphenes constitute the configurations used in the simulations. We report on some results to show the efficiency of the B-splines on different levels.

Erratum to “Multilevel B-Spline Repulsive Energy in Nanomodeling of Graphenes” [Journal of Surface Engineered Materials and Advanced Technology Vol. 4 No. 2 (April 2014) 75-86]

Quantum energies which are used in applications are usually composed of repulsive and attractive terms. The objective of this study is to use an accurate and efficient fitting of the repulsive energy instead of using standard parametrizations. The investigation is based on Density Functional Theory and Tight Binding simulations. Our objective is not only to capture the values of the repulsive terms but also to efficiently reproduce the elastic properties and the forces. The elasticity values determine the rigidity of a material when some traction or load is applied on it. The pair-potential is based on an exponential term corrected by B-spline terms. In order to accelerate the computations, one uses a hierarchical optimization for the B-splines on different levels. Carbon graphenes constitute the configurations used in the simulations. We report on some results to show the efficiency of the B-splines on different levels.

Deformable Registration of Chest Radiographs Using B-spline Based Method with Modified Residual Complexity

Accurate registration of chest radiographs plays an increasingly important role in medical applications.However, most current intensity-based registration methods rely on the assumption of intensity conservation that is not suitable for alignment of chest radiographs. In this study, we propose a novel algorithm to match chest radiographs, for which the conventional residual complexity(RC) is modified as the similarity measure and the cubic B-spline transformation is adopted for displacement estimation. The modified similarity measure is allowed to incorporate the neighborhood influence into variation of intensity in a justified manner of the weight, while the transformation is implemented with a registration framework of pyramid structure. The results show that the proposed algorithm is more accurate in registration of chest radiographs, compared with some widely used methods such as the sum-of-squared-differences(SSD), correlation coefficient(CC) and mutual information(MI)algorithms, as well as the conventional RC approaches.

基于层次B样条的医学图像弹性配准方法

弹性配准是医学图像处理的重要研究内容,为了精确得到图像的局部几何变形,需要大量均匀分布的标记点来表示局部偏差,为此,提出了一种自动、准确的基于区域相似性的标记点选取方法,并利用层次B样条散乱数据插值方法能够解决逼近精度和光滑性的平衡问题的特点,建立一种新的配准方法．实验证明,本文提出的自动标记点的选取方法能够产生准确的表示局部变形的标记点对应性,所建立的配准方法是一种准确、简便和稳定的方法,能够产生光滑、准确的配准变换．

基于B样条的快速弹性图像配准方法

论文提出了一种基于B样条的医学图像快速弹性配准方法。该方法在原有方法的基础上引入“分块计算,部分更新”的策略来提高运行速度。实验结果表明,论文方法与原有方法相比,配准效果相似,但配准速度却显著提高,因此是一种实用的弹性配准方法。

基于互信息量和薄板样条的X射线造影图像弹性配准

本文提出了一种基于互信息量和薄板样条的冠状动脉造影图像弹性配准新方法.该方法不仅解决了传统模板匹配方法中子块图像分解的不确定性问题,而且还克服了常规优化方法易受初值影响而陷入局部最优的问题,有效的保证了解的收敛.试验结果证明本算法能够获得亚像素的配准精度.

基于B样条的弹性点配准方法

为了更好地进行图像弹性点的配准,提出了一种利用Hausdorff距离测度的弹性点配准方法。该方法以B样条为弹性形变模型,并具有较强的抵御杂点影响的能力。在此基础上又提出了序贯更新策略,即通过将源图像和控制点网格进行分块的方法来序贯更新弹性配准参数,从而进一步提高了算法的运算速度。为验证该方法的配准效果,采用该方法进行了合成图像、手写字体和脑部MRI图像的弹性配准实验。实验结果表明,该方法在基于特征的弹性配准应用中具有较好的使用效果。

基于B样条的医学图像配准新算法

目的:提出一种基于B样条的医学图像塔式配准新算法,即RE配准模型。方法:首先实现图像的整体配准,即用一次B样条基函数作为变形函数,对图像进行仿射变换;然后通过均匀增加节点数量及改变变形函数的复杂性,实现图像细节上的配准,这一循环匹配过程主要是通过改变B样条基函数及控制系数复杂性,来实现图像的仿射变换及弹性变换。结果:整个匹配算法体现了图像从整体到细节的匹配思想。结论:实验证明,该方法与单纯的弹性配准方法相比,不仅计算速度有很大的提高,而且有很强的鲁棒性。

基于互信息量的医学图像自动弹性配准

本研究采取一种基于互信息量的分级自动选点方法选取对应特征点集。得到对应点集后 ,利用改进的薄板样条插值方法来进行弹性配准。用此方法 。

图像扭曲变换中高斯基函数的最优参数分析

薄板样条插值TPS是基于标记点的医学图像弹性配准中常用的插值方法,但该方法的扭曲作用是全局的,对于局部扭曲配准会导致匹配精度下降。基于径向基函数的图像变换方法可以解决局部扭曲配准问题,但如何选取径向基函数的参数还没有很好地解决。本文针对以高斯基函数为径向基函数的局部弹性变换问题,讨论了双标志点情况下高斯径向基函数的参数选取方法,该方法可以使双标志点之间的扭曲影响范围最局部化。本文结论用于多标志点的图像配准时可以解决图像局部弹性变换问题,实验结果验证了本文的结论。

基于算术调和均值距离测度的弹性图像配准

针对图像配准中的特征提取问题,提出了一种基于自动选取标记点的弹性图像配准方法。首先,提出了新的相似性测度,算术-调和均值距离测度AHM,图像通过最大化AHM全局配准,然后将图像划分成均匀子块,采用AHM测度刚性配准各对应子块,选取对应子块的中心作为标记点对,最后,用三次均匀B样条实现图像的弹性配准。实验结果表明,AHM是有效的,其配准速度是互信息的2倍。

用无约束优化薄板样条实现平滑的医学图像弹性配准

医学图像弹性配准是医学图像处理的一个重要研究方向。目前采用的方法多是手动选择对应标记点 ,然后用薄板样条插值方法计算配准变换。由于对应点的选取总是存在误差 ,所以配准的准确性受到影响 ,而且手动选点操作繁杂、耗时大。为此 ,我们根据最优化理论 ,改进了薄板样条插值方法 ,并在此基础上采用了一种自动标记点选择方法。将这两者结合 ,我们得到了一种自动、准确、鲁棒性好的配准方法 ,运用此方法进行医学图像的弹性配准 。

基于先验知识和MRF随机场模型的医学图像弹性配准方法

本研究提出了一种新的基于先验知识的弹性配准算法,首次把马尔可夫模型应用于图像的弹性配准方面。为了把关于变形场的先验知识融合到弹性配准过程中,本研究以马尔可夫随机场模型作为理论框架,以B样条为基函数来构造弹性变形模型,以弹性模型的B样条系数作为待估参数,以原图像和变形图像作为已知条件,把弹性变形模型和关于变形场的先验知识有机的融合到了马尔可夫随机场模型中,实现了一种基于变形场先验知识的弹性配准算法。这种新算法因为有变形场的先验知识,所以可以得到更好配准结果。本研究以变形场的平滑作为先验知识,可以有效改善局部极值的状况,提高算法的可靠性和鲁棒性。本研究分别对2D和3D图像进行了试验,试验结果证明了这种算法的有效性。

基于GPU的弹性图像配准方法

通常的弹性配准技术因其计算强度大,消耗时间长,难以满足实时应用的要求.新一代图形处理器(GPU)以其用户友好的可编程性和出色的并行计算能力,为解决该问题提供了新的途径.根据GPU的自身特点,以薄板样条插值作为变换模型,构建了弹性配准计算平台.对二维单模态和多模态的两组图像进行实验,结果表明,相比于CPU,利用GPU可以更为迅速地获得变换参数,对于大尺寸、高分辨率或者多局部形变的图像,GPU的处理速度超出CPU1个数量级以上.

用平滑薄板样条实现医学图象的弹性配准

医学图象弹性配准是医学图象处理的一个重要研究方向 .目前采用的方法多是手动选择对应标记点 ,然后用薄板样条插值方法计算配准变换 .为了降低对应点的选取误差对配准准确性的影响 ,并克服手动选点操作繁杂、耗时大的问题 ,给出了一种准确、快速、鲁棒性好的配准方法 .即对薄板样条插值方法进行平滑处理 ,并在此基础上采用一种半自动标记点选择方法 .运用此方法进行医学图象的弹性配准 ,得到了理想的结果 .实验表明 ,该方法能有效减弱了对应点位置误差对配准结果产生的影响 。