Robust Gradient-Based 3-D/2-D Registration of CT and MR to X-Ray Images

One of the most important technical challenges in image-guided intervention is to obtain a precise transformation between the intrainterventional patient's anatomy and corresponding preinterventional 3-D image on which the intervention was planned. This goal can be achieved by acquiring intrainterventional 2-D images and matching them to the preinterventional 3-D image via 3-D/2-D image registration. A novel 3-D/2-D registration method is proposed in this paper. The method is based on robustly matching 3-D preinterventional image gradients and coarsely reconstructed 3-D gradients from the intrainterventional 2-D images. To improve the robustness of finding the correspondences between the two sets of gradients, hypothetical correspondences are searched for along normals to anatomical structures in 3-D images, while the final correspondences are established in an iterative process, combining the robust random sample consensus algorithm (RANSAC) and a special gradient matching criterion function. The proposed method was evaluated using the publicly available standardized evaluation methodology for 3-D/2-D registration, consisting of 3-D rotational X-ray, computed tomography, magnetic resonance (MR), and 2-D X-ray images of two spine segments, and standardized evaluation criteria. In this way, the proposed method could be objectively compared to the intensity, gradient, and reconstruction-based registration methods. The obtained results indicate that the proposed method performs favorably both in terms of registration accuracy and robustness. The method is especially superior when just a few X-ray images and when MR preinterventional images are used for registration, which are important advantages for many clinical applications.      

基于多尺度分解的k邻域随机查找快速图像修复

该文提出一种基于多尺度分解的k邻域随机查找快速图像修复方法。基于双边滤波下采样分解图像,从图像最粗糙层开始,对每一粗糙层采用基于最小堆的k邻域随机查找算法快速搜索最佳匹配块,利用鲁棒优先级函数确定下一待修复块。每一粗糙层修复后用双边滤波上采样重建下一粗糙层,迭代得到最终的修复结果。与相关工作比较,所提方法的修复结果能够保持图像的细节和边缘信息,取得更高的修复质量。利用客观指标评价修复结果。实验结果表明该方法有效易行,修复的图像具有良好的可视效果。     

仿射不变子空间特征及其在图像匹配中的应用

提出了一种新的基于子空间的仿射不变图像匹配算法。首先,分析了仿射不变特征空间的非线性几何结构,给出了图像特征的仿射不变子空间表示模型。其次,根据目标特征矩阵由它的正交投影矩阵唯一确定这一特性实现特征的全局一致性匹配。最后,通过计算同一目标在仿射变换下的子空间几何距离度量达到匹配识别的目的。模拟变换匹配实验和真实图像序列实验结果表明:所提出的匹配方法对仿射变化下的图像目标匹配具有很好的适应性和较高的鲁棒性。     

A new image registration method for grey images

The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.     

基于分块形状特征的异源图像匹配方法

在基于特征的异源图像匹配中,由于成像原因导致的轮廓不完整会使得匹配难度增加。针对这一问题,提出了一种基于分块形状特征的匹配方法。首先从基准图和实时图中提取轮廓特征并对其进行分块,然后提取分块特征并归一化,最后采用加权相似性度量实现匹配定位。由于利用候选目标区域信息排除了虚警,进一步提高了匹配的正确性和鲁棒性。采用该方法对红外与可见光图像进行了测试。结果表明,本文方法具有较好的匹配性能。     

HOG特征混合模型结合隐SVM的感兴趣目标检测定位算法

针对传统基于梯度方向直方图特征检测算法对解决目标模型单一、发生形变、存在遮挡及目标受干扰下定位困难的问题,提出一种基于HOG特征混合模型结合隐SVM的感兴趣目标检测算法。首先利用用训练图像的HOG特征金字塔表示得到包含感兴趣目标根模型、部件模型和对应可变形部件特征表示,该模型不仅描述目标的整体轮廓,而且能够捕捉到更为精细的目标部件轮廓,在一定程度上提高了检测算法在目标姿态复杂情况下的鲁棒性。然后利用HOG特征混合特征训练部件检测分类器LSVM(Latent Support Vector Machine)。最后通过动态规划和距离转换算法在测试图上扫描出与可变形部件模型相匹配的区域,实现感兴趣目标的检测定位。经过多组实验结果表明,所提出的算法能较好地解决目标在发生较大形变和存在遮挡等复杂姿态下的定位问题。      

基于全变分Retinex及梯度域雾天图像增强算法

为提高雾天图像增强的对比度并保持颜色恒常性,提出了基于全变分Retinex及梯度域雾天图像增强算法。首先,采用高斯—赛德尔GS（Gauss-Seidel）迭代算法对基于Retinex的全变分能量泛函数进行求解,从而有效地保持颜色恒常性;其次,采用相对梯度与绝对梯度相结合的方式拉伸雾天图像较亮处的梯度, 在全变分Retinex理论下重建增强后的雾天图像,并将该增强算法应用到彩色图像;最后,加权融合基于全变分Retinex增强算法与梯度域增强算法的增强结果,使得增强结果既能提高对比度又能保持色彩恒常性。实验结果表明,本算法提高了雾天图像增强后的对比度和清晰度,具有颜色恒常性、颜色保真高等特性。     

基于指纹分类的模式匹配

深入研究了指纹匹配算法.利用指纹分类获得的信息来进行指纹匹配,实现了一种基于中心点和三角点的快速点模式指纹匹配算法,成功地解决了指纹匹配中的图像旋转、平移和形变不变性问题,并具有良好的快速性.该方法以待识和模板指纹图像的中心点为匹配参考点,并根据它们的位置与方向信息来计算旋转和平移参数.然后将待识图像相对于模板图像进行旋转和平移,进而进行点模式匹配.     

基于梯度积分投影和最大期望算法的人眼定位

针对传统积分投影方法易受眉毛、睫毛、阴影、遮挡及噪声等干扰的问题,提出了一种梯度积分投影与最大期望(EM)算法相结合的人眼精确定位方法,可以在人脸图像中分割出人眼区域,并精确定位人眼位置。首先,采用一种新的梯度算子计算人脸图像的行梯度积分投影粗略定位人眼区域;然后计算人眼区域的列梯度积分投影函数,用EM算法将所得列梯度积分投影函数曲线拟合成两个高斯曲线,并根据高斯曲线精确分割出人眼窗口;最后,利用我们提出的加权质心法在所得人眼窗口中精确定位双眼位置。在YaleB人脸数据库及自采数据库上的实验结果表明,本文方法不易受眉毛及噪声干扰,并能有效克服眼睑和睫毛的遮挡,对不同光照条件及头部姿态都有很好的鲁棒性。     

A new approach for nonlinear distortion correction in endoscopicimages based on least squares estimation

Images captured with a typical endoscope show spatial distortion, which necessitates distortion correction for subsequent analysis. In this paper, a new methodology based on least squares estimation is proposed to correct the nonlinear distortion in the endoscopic images. A mathematical model based on polynomial mapping is used to map the images from distorted image space onto the corrected image space. The model parameters include the polynomial coefficients, distortion center, and corrected center. The proposed method utilizes a line search approach of global convergence for the iterative procedure to obtain the optimum expansion coefficients. A new technique to find the distortion center of the image based on curvature criterion is presented. A dual-step approach comprising token matching and integrated neighborhood search is also proposed for accurate extraction of the centers of the dots contained in a rectangular grid, used for the model parameter estimation. The model parameters were verified with different grid patterns. The distortion-correction model is applied to several gastrointestinal images and the results are presented. The proposed technique provides high-speed response and forms a key step toward online camera calibration, which is required for accurate quantitative analysis of the images.     

基于改进RANSAC算法的全景图像拼接技术

现实场景中相机获取的图像视角范围往往是有限的,而实际需求又要求得到场景的全 景图,针对日常生活和工业生产中对全景图像的需求以及传统的RANSAC(random sample consensus)算法在图像配准环节因为迭代次数没有上限导致出现误匹配点对且配准 速度不高的缺陷,提出了一种改进RANSAC算法来提高全景图像拼接的效率。改进RANSAC 算法通过检测圆内的点来寻找一个最优数据检测模型,并通过粒子群算法不断更新迭代圆心 的坐标,最终得到一个最佳的匹配模型,消除特征点匹配环节出现的异常值,在提高特征 点配准的准确率的同时降低算法复杂度。在对多组图像进行拼接的实验表明,本文提出的改 进RANSAC算法相较于其他几种算法平均正确匹配率提高了9.057%, 同时算法的平均配准速率提高了5.173 s,实 现了较鲁棒的全景图像拼接效果。     

Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI

Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.     

基于区域特征与灰度交叉相关的序列图像拼接

针对具有平移、旋转、缩放变换的序列图像连续拼接,提出一种将基于区域特征的配准算法和基于灰度交叉相关的配准算法相结合的拼接算法。该算法用迭代阈值分割算法提取区域．利用区域特征进行配准,建立初始的同名区域对;然后以同名区域对的质心点作为特征点,基于图像的灰度信息,选择交叉相关准则作为度量;最后得到图像间的精确变换关系,实现序列图像的拼接。实验结果表明,算法配准率高,鲁棒性强。     

基于特征序列匹配函数的快速图像匹配

为解决在导弹制导、目标搜寻等领域中图像快速匹配的问题,提出一种基于图像局部特征序列的匹配函数,以加快图像匹配速度。该算法将匹配图像和查找图像分为若干相同大小的子图片,并计算它们的局部特征,形成一个带有特征的序列,利用提出的特征序列匹配函数,计算两种图像内容的特征序列下的匹配情况,该匹配函数能够利用匹配图像本身的特征信息提高图像匹配的查找速度。由于匹配图像特征序列之间存在关联,在匹配失效的情况下,不用再从查找图像的特征序列起始点重新进行查找匹配,而是根据匹配函数,计算出新的匹配点,从新的匹配点进行匹配,加快图像匹配的速度。     

An efficient search strategy for block motion estimation usingimage features

Block motion estimation using the exhaustive full search is computationally intensive. Fast search algorithms offered in the past tend to reduce the amount of computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on this assumption: the mean absolute difference (MAD) distortion function increases monotonically as the search location moves away from the global minimum. Essentially, this assumption requires that the MAD error surface be unimodal over the search window. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighborhood around the global minimum. Consequently, one simple strategy, but perhaps the most efficient and reliable, is to place the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of certain feature points. After applying a feature detecting process to each frame to extract a set of feature points as matching primitives, we have extensively studied the statistical behavior of these matching primitives, and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient. A beautiful point of our approach is that the proposed search algorithm can work together with other block motion estimation algorithms. Results of our experiment on applying the present approach to the block-based gradient descent search algorithm (BBGDS), the diamond search algorithm (DS) and our previously proposed edge-oriented block motion estimation show that the proposed search strategy is able to strengthen these searching algorithms. As compared to the conventional approach, the new algorithm, through the extraction of image features, is more robust, produces smaller motion compensation errors, and has a simple computational complexity.     

An Automatic Image Registration for Applications in Remote Sensing

This paper deals with a major problem encountered in the area of remote sensing consisting of the registration of multitemporal and/or multisensor images. In general, such images have different gray-level characteristics, and simple techniques such as those based on correlation cannot be applied directly. In this work, a new automatic satellite image registration approach is proposed. This technique exploits the invariant relations between regions of a reference and a sensed image, respectively. It involves an edge-based selection of the most distinctive control points (CPs) in the reference image. The search for the corresponding CPs in the sensed image is based on local similarity detection by means of template matching according to a combined invariants-based similarity measure. The final warping of the images according to the selected CPs is performed by using the thin-plate spline interpolation. The procedure is fully automatic and computationally efficient. The proposed algorithm for this technique has been successfully applied to register multitemporal SPOT and synthetic aperture radar images from urban and agricultural areas. The experimental results demonstrate the robustness, efficiency and accuracy of the algorithm.     

Automatic watershed segmentation of randomly textured color images

A new method is proposed for processing randomly textured color images. The method is based on a bottom-up segmentation algorithm that takes into consideration both color and texture properties of the image. An LUV gradient is introduced, which provides both a color similarity measure and a basis for applying the watershed transform. The patches of watershed mosaic are merged according to their color contrast until a termination criterion is met. This criterion is based on the topology of the typical processed image. The resulting algorithm does not require any additional information, be it various thresholds, marker extraction rules, and suchlike, thus being suitable for automatic processing of color images. The algorithm is demonstrated within the framework of the problem of automatic granite inspection. The segmentation procedure has been found to be very robust, producing good results not only on granite images, but on the wide range of other noisy color images as well, subject to the termination criterion.     

一种改进的宽基线图像SURF算法

通过对SURF特征点匹配过程的研究,提出一种改进的宽基线图像SURF算法。提取SURF特征点,在特征点匹配过程中采用改进方法计算图像间的尺寸、旋转和平移参数,减少匹配点搜索范围,运用RANSAC算法剔除误匹配点对。实验表明本算法在图像间有较大差别时降低了误匹配率,具有良好的实时性,达到了较好的应用效果。     

基于梯度向量流场的眼睛特征提取

根据人脸图像的边缘梯度图提出了一种新的基于梯度向量流场的眼睛特征提取方法。该算法改进原有的梯度向量流迭代方程求解梯度向量流场,以梯度向量流场中的汇点作为候选点,通过人脸器官的几何位置关系等方法评价候选点以定位眼球。该算法能较好地容忍一定的光照变化、人脸的小角度倾斜和旋转、闭眼和眼镜等干扰。在具有以上干扰的ORL人脸库的400幅图像上的实验证明,该算法具有较好的眼睛特征抽取能力。