基于互信息多步骤优化的医学图像配准

提出了一种新的基于互信息(Mutual Information, MI)的多步骤优化的配准方法。计算输入图像的梯度值，减少了图像的内在信息而使轮廓更为清晰。设计了多步骤的配准框架，优化了配准的收敛过程，使用完整的图像进行有限次的传统配准方法的微调，以实现高精度。为了验证该方法的有效性，分别使用单模、多模和时间序列的方法对临床医学数据进行了实验，与传统的MI配准方法相比，基于互信息的多步骤优化的配准方法具有更高的有效性和精确度。     

基于互信息与边缘互距离的医学图像配准

提出了一种基于互信息与边缘互距离信息的医学图像配准新测度。该种测度既利用了待配准图像间的灰度互信息,又利用了图像边缘间的互距离均值和互距离方差空间信息,从而改进了互信息测度。实验证明这种测度得到的配准参数曲线光滑且峰值尖锐,收敛范围宽,对图像大小有更强的鲁棒性,在图像互信息值一样的情况下,仍有辨识能力。     

基于小波分解和互信息的图像配准方法

根据小波分解和互信息测度的原理,提出一种快速的图像配准方法。首先,对原图像进行小波分解,在保证配准精度下对分解图像进行灰度压缩,以减少配准参数的计算量,并利用最大互信息准则和下降单纯形的搜索策略找到最优配准参数实现图像配准。实验结果表明,这种图像配准方法能在保证配准精度条件下,提高配准的速度。     

互信息驱动的有限元医学图像配准方法

针对医学图像中软组织形变复杂多样的特点,在配准过程中以离散化的有限单元为基本单位模拟和预测整个弹性体产生的形变,将配准过程视为有限元求解二维平面应力问题并对有限元能量函数进行改进,选取高精度和鲁棒性良好的互信息(MI)为测度应用到方程求解中。为提高算法效率,在配准过程中采用多分辨率策略进行优化。通过对放疗中医学图像进行配准实验并与已有方法进行比较,得到的配准结果更优,对刚体位移更敏感并提高了速度,表明该方法是一种具有高精度和高效率的配准方法。     

基于数学形态学的PET图像背景噪声处理算法*

基于数学形态学的背景噪声处理算法通过图像二值化、二值形态学处理及背景替换消除背景噪声.其减小了互信息配准测度的局部极值,改善了配准测度.     

基于互信息和二级搜索的图像配准

基于互信息的图像配准方法,已经广泛应用于图像配准领域.但互信息图像配准方法容易受到局部极值的干扰,难以得到最优解.对互信息图像配准中互信息的表征、图像插值方法以及优化搜索算法三个要素做了探讨,尤其针对常用的Powell搜索算法的不足,提出了基于互信息和二级搜索的图像配准算法.该算法以标准互信息为图像相似测度,利用PV插值法平滑搜索空间,采用Simplex算法进行一级粗配准,采用Powell算法进行二级精配准.仿真结果表明,二级搜索配准算法能够有效地克服局部极值,提高计算速度,用于大差异图像配准.     

尺度空间图像的互信息与配准

提出基于多尺度图像的互信息测度的配准方法,主要讨论这种方法的性能。提出这种方法的主要目的是在保证这种方法的准确性和鲁棒性的同时加快配准速度,并且具有强抗噪性。将这种方法在MR图像和CT图像上进行检验。这是首次将属性尺度空间与互信息结合,实验表明多尺度图像的互信息测度是一种行之有效的配准方法。     

融合SIFT特征的熵图估计医学图像非刚性配准

配准准确性是医学图像配准算法的一项重要指标,像素灰度是目前图像配准中广泛使用的特征,但是灰度特征来源单一,而且忽略空间信息,在一些情况下容易产生误配。针对这个问题,本文提出一种融合SIFT特征的熵图估计医学图像非刚性配准算法。该算法首先使用基于互信息的刚性配准算法对两幅待配准图像进行粗配;然后,在采样点上提取像素灰度和SIFT高维特征,并在此基础上构造k-最邻近图(kNNG);最后,使用k-最邻近图来估计α互信息(αMI)。实验结果表明:和传统的基于互信息和像素灰度的刚性配准算法,基于熵图估计和单一像素灰度特征的非刚性配准算法相比,本文提出的算法具有更高的配准准确性。     

互信息配准的一种改进算法

在传统的互信息配准方法中,由于重叠面积的变化会引起配准测度的不稳定,导致误配准.本文提出了一种改进的方法,用"面积补偿"的原理来保证参考图像和待配准图像相对平移时重叠面积保持基本不变,有效地增加了配准测度在配准过程中的鲁棒性.通过对比实验证实了此方法的有效性.     

基于分块互信息和量子粒子群算法的图像配准

多模图像的配准是图像融合等图像处理需要先行解决的问题.本文提出了一种基于分块互信息和量子粒子群的配准方法,在配准中利用分块互信息值为相似性测度,并用量子粒子群算法求解配准所需的空间变换参数.实验表明:该方法能够避免陷入局部极小值而且速度得到明显改善,其运用于多模图像配准,可以得到理想的效果.     

Multimodality image registration by maximization of quantitative–qualitative measure of mutual information

This paper presents a novel image similarity measure, referred to as quantitative–qualitative measure of mutual information (Q-MI), for multimodality image registration. Conventional information measures, e.g., Shannon's entropy and mutual information (MI), reflect quantitative aspects of information because they only consider probabilities of events. In fact, each event has its own utility to the fulfillment of the underlying goal, which can be independent of its probability of occurrence. Thus, it is important to consider both quantitative (i.e., probability) and qualitative (i.e., utility) measures of information in order to fully capture the characteristics of events. Accordingly, in multimodality image registration, Q-MI should be used to integrate the information obtained from both the image intensity distributions and the utilities of voxels in the images. Different voxels can have different utilities, for example, in brain images, two voxels can have the same intensity value, but their utilities can be different, e.g., a white matter (WM) voxel near the cortex can have higher utility than a WM voxel inside a large uniform WM region. In Q-MI, the utility of each voxel in an image can be determined according to the regional saliency value calculated from the scale-space map of this image. Since the voxels with higher utility values (or saliency values) contribute more in measuring Q-MI of the two images, the Q-MI-based registration method is much more robust, compared to conventional MI-based registration methods. Also, the Q-MI-based registration method can provide a smoother registration function with a relatively larger capture range. In this paper, the proposed Q-MI has been validated and applied to the rigid registrations of clinical brain images, such as MR, CT and PET images.     

直方图估计互信息在非刚性图像配准中的应用

提出了一种对非刚性三维医学图像进行弹性配准的新方法,配准判据是基于二维联合直方图估计得到的两幅图像灰度之间的互信息．首先最大化全局互信息,实现两幅图像之间的全局仿射配准,然后将两幅图像的重叠部分均匀地划分成互为重叠的体积子块,再最大化对应体积子块的图像灰度之间的互信息,实现每对对应子块的局部刚体配准,将每个子块的中心作为一一对应的控制点．最后利用这些控制点对结合薄平板样条插值法实现全局弹性配准．     

基于最大互信息的多模医学图象配准

介绍了一种基于最大互信息原理的图象配准技术,并就实施最大互信息配准法的一些重要技术问题进行了研究,其中包括不增加新数据点的格点采样子集、不产生分数灰度值的ＰＶ插值技术和出界点策略等。该方法在搜索策略上采用了无需计算梯度的Ｐｏｗｅｌｌ算法。由于计算互信息的关键技术与有效的搜索策略的结合,使得该方法能快速、准确地实现多模医学图象的配准。用该方法对７个病人的４１套ＣＴ－ＭＲ和３５套ＭＲ－ＰＥＴ３Ｄ全脑数     

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.     

广义信息熵测度在医学图像配准中的应用

针对互信息测度在配准医学图像时易陷入局部极值的缺点,将Shannon熵扩展到广义熵,提出了三种基于广义熵的信息测度。对于收敛性能的评价,提出收敛宽度和收敛半径的概念。通过人体脑部CT/MR和MR-T1/T2图像的刚体配准实验,从计算时间、收敛性能和配准精度方面,对归一化互信息、广义熵信息测度进行了比较与分析。实验结果表明,在不损失计算时间和配准精度的前提下,广义信息熵测度SRI_0.9和GMI_0.9的收敛性能优于归一化互信息测度,对噪声有很强的鲁棒性。     

Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing images is a difficult task. To solve this, mutual information (MI)-based methods have been a preferred choice, as MI represents a measure of statistical dependence between the two images. However, MI only considers original grey information and neglects spatial information in the calculation of the probability distribution. In this paper, a novel similarity metric based on rotationally invariant regional mutual information (RIRMI) is proposed. The RIRMI metric is constructed by combining MI with a regional information based on the statistical relationship between rotationally invariant centre-symmetric local binary patterns of the images. The similarity metric based on RIRMI considers not only the spatial information, but the effect of the local grey variations and rotation changes on computing probability density function as well. The proposed method is tested on various simulated remote sensing images (5–30 m GSD) and real remote sensing images (2–30 m GSD) which are taken at different sensors, spectral bands, and times. Results verify that RIRMI is more robust and accurate than the common MI-based registration method.     

Interpolation Artefacts in Mutual Information-Based Image Registration

Image registration requires the transformation of one image to another so as to spatially align the two images. This involves interpolation to estimate gray values of one of the images at positions other than the grid points. When registering two images that have equal grid distances in one or more dimensions, the grid points can be aligned in those dimensions for certain geometric transformations. Consequently, the number of times interpolation is required to compute the registration measure of two images is dependent on the image transformation. When an entropy-based registration measure, such as mutual information, is plotted as a function of the transformation, it will show sudden changes in value for grid-aligning transformations. Such patterns of local extrema impede the registration optimization process. More importantly, they rule out subvoxel accuracy. In this paper, two frequently applied interpolation methods in mutual information-based image registration are analyzed, viz. linear interpolation and partial volume interpolation. It is shown how the registration function depends on the interpolation method and how a slight resampling of one of the images may drastically improve the smoothness of this function.