虚拟结肠镜中皱襞预匹配的结肠配准

目的 虚拟结肠镜是一种采用CT或者MRI图像重建出结肠3维结构,通过漫游虚拟结肠来检测结肠组织,一般用于早期结直肠癌筛查。结肠配准能够有效提高息肉检测的效率和精确度,但由于仰卧和俯卧位下的结肠图像形变太大,现有的配准方案中特征点的提取没有考虑到较多特殊情况,因此需要寻找一个新的配准方案完成完整的结肠配准。方法 提出了一种新的结肠图像配准方法,能够完成不同体位获取的虚拟结肠图像之间的配准。首先提取可以反映结肠结构信息的皱襞特征,用模板匹配和特征匹配方法找出两幅结肠中匹配的皱襞对。然后将匹配对的中心点作为标记点,做基于标记点的非刚性粗配准,最后将两幅图做B样条配准完成细配准。这种方法能够将结肠内部较大的形变先矫正,使得两幅图之间的形变缩小到一定范围,然后利用传统配准方法能够完成配准。结果 在5套数据中,找到能够成功匹配的皱襞区域数量占所有分割出的皱襞区域总数量的62%左右,匹配错误率为4.7%左右。完成皱襞粗配准后,结肠形变明显趋于一致,灰度值相对误差减小,最终完成了结肠配准。结论 先进行皱襞匹配再做基于匹配好的皱襞的映射关系做结肠配准,能够将存在较大形变的两套结肠匹配起来。在之后的工作中需要量化特征点选取对配准结果的影响,同时在做配准评估时,单纯采用灰度差值不能很好完成评估,因为灰度特征只能一定程度反映整体差异,不能很好体现结构差异,需要添加其他评估标准辅助配准评估。     

Hypercomplex Mathematical Morphology

The natural ordering of grey levels is used in classical mathematical morphology for scalar images to define the erosion/dilation and the evolved operators. Various operators can be sequentially applied to the resulting images always using the same ordering. In this paper we propose to consider the result of a prior transformation to define the imaginary part of a complex image, where the real part is the initial image. Then, total orderings between complex numbers allow defining subsequent morphological operations between complex pixels. More precisely, the total orderings are lexicographic cascades with the local modulus and phase values of these complex images. In this case, the operators take into account simultaneously the information of the initial image and the processed image. In addition, the approach can be generalized to the hypercomplex representation (i.e., real quaternion) by associating to each image three different operations, for instance directional filters. Total orderings initially introduced for colour quaternions are used to define the evolved morphological transformations. Effects of these new operators are illustrated with different examples of filtering.     

Augmented reality-based robot teleoperation system using RGB-D imaging and attitude teaching device

Augmented reality (AR)-based programming using the demonstration method has been widely studied. However, studies on AR-based programming for remote robots are lacking because of the limitation of human–computer interaction. This paper proposes an AR-based robot teleoperation system and method using RGB-D imaging and an attitude teaching device. By sending the color and depth images of the remote robot environment to the local side, the operators can complete the teleoperation of the robot at the local side. First, the operators select key positions on the motion path of the robot endpoint from color images via a mouse, and the computer calculates the 3D coordinates of these key points in the robot base coordinate system to complete the position teaching process. In the robot attitude teaching process, the AR technology is used to superimpose the virtual robot model onto the color images of the robot teleoperation environment, so as to make the virtual robot endpoint to move along the teaching path. An operator can use the portable attitude teaching device designed in this study to control the robot movement parameters, such as the attitude and motion speed, during the movement of the virtual robot. After the position and attitude teaching processes, the robot movement trajectory can be generated. To make the base coordinate system of the virtual model consistent with that of the physical robot, we propose an online AR registration method, which does not require manually placing the AR registration marker. The proposed AR-based robot teleoperation system can quickly and easily complete robot teleoperation at the local side.     

基于相关性的病理切片图像配准

尽管病理切片图像对配准的要求比较高,而现存的配准方法却难免会产生失配现象。为解决这一问题,提出了一种基于相关性的病理切片图像配准新算法。该算法是采用基于图片中两平行列(行)间的数据来抽取特征的方法。为了避免“干扰”的累积,该算法选择了差值曲线中最强的特征——最大包来作为模板的基元,并根据图片内容的相关性,利用最大包的分布作为模板来进行图像配准,从而降低了“干扰”的影响,使算法配准的稳定度比以前一些算法有较大的提高。实验证明,该算法不仅比较规整,稳定性高,而且计算速度也较快,是一种比较实用的图像配准算法。     

Validation of mutual information-based registration of CT and bone SPECT images in dual-isotope studies

The registration of computed tomography (CT) and nuclear medicine (NM) images can substantially enhance patient diagnosis as it allows for the fusion of anatomical and functional information, as well as the attenuation correction of NM images. However, irrespective of the method used, registration accuracy depends heavily on the characteristics of the images that are registered and the degree of similarity between them. This poses a challenge for registering CT and NM images as they have very different characteristics and content. To address the particular problem of registering single photon emission computed tomography (SPECT) oncology studies with corresponding CT, we have proposed to perform a dual-isotope study with simultaneous injection of a tumor tracer and a bone imaging agent to obtain a tumor SPECT and a bone SPECT image that are inherently registered. As bone structures are generally visible in both CT and bone SPECT, performing registration of these images will be more easily attainable than registration of CT and tumor SPECT. By subsequently applying the spatial transformation determined from this registration to the tumor SPECT acquired from the same dual-isotope study, the optimal alignment between the CT and tumor SPECT images can be obtained. In this paper, we present the proof-of-concept of the proposed approach, the MI-based algorithm employed, and the techniques used to select the algorithm's parameters. Our objectives are to show the feasibility of CT and bone SPECT registration using this algorithm and to validate quantitatively the results generated using clinical data.     

A generalized registration method for augmented reality systems

In augmented reality systems, registration is one of the most difficult problems currently limiting their applications. In this paper, we propose a generalized registration method using projective reconstruction technique in computer vision. This registration method is composed of embedding and tracking. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In this stage, any arbitrary two unrelated images or any 3×4 projective matrices with rank 3 can be used to calculate the 3D pseudo-projective coordinates of the four specified points. In the tracking process, these 3D pseudo-projective coordinates are used to track the four specified points to compute the registration matrix for augmentation. The proposed method is simple, as only four points need to be specified at the embedding stage, and the virtual object can then be easily augmented onto a real scene from a video sequence. One advantage is that the virtual objects can still be superimposed on the specified regions even when the regions are occluded in the video sequence. Another advantage of the proposed method is that the registration errors can be adjusted in real-time to ensure that they are less than certain thresholds that have been specified at the initial embedding stage. Several experiments have been conducted to validate the performance of the proposed generalized method.     

Image shadow removal using pulse coupled neural network

This paper introduces an approach for image shadow removal by using pulse coupled neural network (PCNN), based on the phenomena of synchronous pulse bursts in the animal visual cortexes. Two shadow-removing criteria are proposed. These two criteria decide how to choose the optimal parameter (the linking strength /spl beta/). The computer simulation results of shadow removal based on PCNN show that if these two criteria are satisfied, shadows are removed completely and the shadow-removed images are almost as the same as the original nonshadowed images. The shadow removal results are independent of changes of intensities of shadows in some range and variations of the places of shadows. When the first criterion is satisfied, even if the second criterion is not satisfied, as to natural grey images that have abundant grey levels, shadows also can be removed and PCNN shadow-removed images retain the shapes of the objects in original images. These two criteria also can be used for color images by dividing a color image into three channels (R, G, B). For shadows varying drastically, such as the noisy points in images, these two criteria are still right, but difficult to satisfy. Therefore, this approach can efficiently remove shadows that do not include the random noise.     

基于平衡系数的Active Demons非刚性配准算法

经典的Active demons算法利用参考图像和浮动图像的梯度信息作为驱动力,并使用均化系数调节两种驱动力之间的强度.该算法克服了Demons算法单一使用参考图像的梯度信息作为驱动力的缺点,但是Active demons算法中的均化系数无法同时兼顾大形变和小形变区域的准确配准,还会导致配准的收敛速度和精确度相互制约的问题.为此,本文提出一种新的Active demons非刚性配准算法.提出的算法在Active demons扩散方程中引入一个称为平衡系数的新参数,与均化系数联合调整驱动力,不仅可以兼顾图像中同时具有的大形变和小形变区域的准确配准,而且在一定程度上缓和了收敛速度和精确度相互制约的问题.为了进一步提高配准的收敛速度和精确度,避免陷入局部极值,在新的配准算法的实现中引入由粗到细的多分辨率策略.在Checkboard测试图像、自然图像和医学图像上的实验结果表明,提出的算法较经典的Active demons算法收敛速度更快,配准精度平均提高了54.28%,接近最新的TV-L1光流场图像配准算法的配准精度,解决了Active demons算法存在的问题.     

A semi-automatic algorithm for grey level estimation in tomography

Discrete tomography focuses on the reconstruction of images that contain only a few grey levels from their projections. By incorporating prior knowledge about the set of grey levels, the required number of projections can be reduced substantially. In practical applications, however, the number of grey levels is often known in advance, yet the actual grey level values are unknown. Moreover, it can be difficult to estimate these grey levels, particularly if only a small number of projections are available. In this paper, we propose a semi-automatic approach for grey level estimation that can be used as a preprocessing step before applying discrete tomography algorithms. After an initial, non-discrete reconstruction has been computed, the user first selects some regions that are likely to correspond with the respective grey levels. The fact that these regions should be constant in the original image is then used as prior knowledge in the grey level estimation algorithm. We present the results of a series of simulation experiments, demonstrating the accuracy and robustness of our approach.     

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.     

2D/3D image registration using regression learning

In computer vision and image analysis, image registration between 2D projections and a 3D image that achieves high accuracy and near real-time computation is challenging. In this paper, we propose a novel method that can rapidly detect an object’s 3D rigid motion or deformation from a 2D projection image or a small set thereof. The method is called CLARET (Correction via Limited-Angle Residues in External Beam Therapy) and consists of two stages: registration preceded by shape space and regression learning. In the registration stage, linear operators are used to iteratively estimate the motion/deformation parameters based on the current intensity residue between the target projection(s) and the digitally reconstructed radiograph(s) (DRRs) of the estimated 3D image. The method determines the linear operators via a two-step learning process. First, it builds a low-order parametric model of the image region’s motion/deformation shape space from its prior 3D images. Second, using learning-time samples produced from the 3D images, it formulates the relationships between the model parameters and the co-varying 2D projection intensity residues by multi-scale linear regressions. The calculated multi-scale regression matrices yield the coarse-to-fine linear operators used in estimating the model parameters from the 2D projection intensity residues in the registration. The method’s application to Image-guided Radiation Therapy (IGRT) requires only a few seconds and yields good results in localizing a tumor under rigid motion in the head and neck and under respiratory deformation in the lung, using one treatment-time imaging 2D projection or a small set thereof.     

基于Hausdorff距离图象配准方法研究

图象配准是图象融合的一个重要步骤，为此提出了一种自动图象配准算法，该算法从两幅待配准的图象中分别抽取特征点，然后选用Hausdorff距离对两特征点集进行匹配，得到点集间的仿射变换，从而实现图象的自动配准，此算法以特征点而不是物体边缘计算仿射变换，大大降低了计算Hausdorff距离的运算量；同时，基于Hausdorff距离的图象匹配只需要点集之间的对应，而无须点与点的对应，因而可以使用于存在较大物体形变的情况，即完成两幅差异较大图象的配准，实验结果证明了算法的有效性。     

基于灰色语言变量的移动银行网络安全风险评估方法

针对移动银行网络安全风险评估问题,提出一种灰色加性语言有序加权平均算子的网络安全多人评估模型。首先在灰色模糊数中将语言变量融入其模部形成灰色语言变量,在此基础上界定了灰色加性语言变量间的运算法则、可能度以及几种灰色加性语言信息集成算子,最后构建了移动银行网络安全多人评估模型。仿真示例验证了模型的可行性。     

A novel quantum representation for log-polar images

The power of quantum mechanics has been extensively exploited to meet the high computational requirement of classical image processing. However, existing quantum image models can only represent the images sampled in Cartesian coordinates. In this paper, quantum log-polar image (QUALPI), a novel quantum image representation is proposed for the storage and processing of images sampled in log-polar coordinates. In QUALPI, all the pixels of a QUALPI are stored in a normalized superposition and can be operated on simultaneously. A QUALPI can be constructed from a classical image via a preparation whose complexity is approximately linear in the image size. Some common geometric transformations, such as symmetry transformation, rotation, etc., can be performed conveniently with QUALPI. Based on these geometric transformations, a fast rotation-invariant quantum image registration algorithm is designed for log-polar images. Performance comparison with classical brute-force image registration method reveals that our quantum algorithm can achieve a quartic speedup.     

变参数active demons算法下的多通道弥散张量图像配准

目的 弥散张量图像（DTI）配准不仅要保证配准前后图像解剖结构的一致性，还要保持张量方向的一致性。demons算法下的多通道DTI配准方法可充分利用张量的信息，改善配准质量，但大形变区域配准效果不理想，收敛速度慢。active demons算法能够加快收敛速度，但图像的拓扑结构容易改变。由此提出一种变参数active demons算法下的多通道DTI配准方法。方法 综合active demons算法中平衡系数能加快收敛速度、均化系数能提高DTI配准精度的优点，手动选择一个均化系数，并在算法收敛过程中随着高斯核的减小动态调整平衡系数。在配准开始时采用较小的平衡系数获得较快的收敛速度，随着收敛的加深逐渐增大平衡系数获得较小的配准误差。结果 active demons方法能改善DTI大形变区域的配准问题，但均化系数太小会改变图像拓扑结构。固定均化系数，引入单一的平衡系数能加快收敛速度，但会导致拓扑结构改变。变参数active demons方法有效提高了配准的收敛速度，明显改善大形变区域的配准效果，同时能保持图像拓扑结构不变。变参数active demons配准后的10组数据均获得最小均方差（MSE）和最大特征值特征向量对重叠率（OVL），配准精度最高。在0.05的配对样本t检验水平下，变参数active demons和active demons方法配准后的MSE、OVL的差异均有统计学意义；变参数active demons和demons方法配准后的MSE、OVL的差异均有统计学意义（p<0.05）。结论 变参数active demons算法下的多通道DTI配准方法明显提高了配准精度和速度，改善了demons方法不能有效配准大形变区域的问题，同时能够保持配准前后图像的拓扑结构，尤其适合个体间形变较大的DTI配准。     

Adaptive gray scale mapping to reduce registration noise in difference images

Difference images are used in various image processing applications such as change detection, radar imaging, remote sensing, and biomedical image analysis. The difference image, or difference picture, is found by subtracting one image from another. One practical problem with difference images is that, if the images are not in perfect spatial registration before subtraction, their difference image will contain artifacts caused by incomplete cancellation of the unchanged background objects. These artifacts (registration noise) show up as extraneous light and dark regions on either side of the background objects. Usually, this noise is reduced by either smoothing (blurring), or thresholding the difference image. This paper describes a new method to reduce registration noise using adaptive gray scale mapping. This simple digital filter reduces registration noise as well as, or better than, previous methods, with less degradation of the actual differences between the images.     

基于Legendre矩的CT及MR医学图象融合方法

为了提高CT、MR多模态医学图象配准、融合的精度和速度,提出了基于Legendre矩的CT和MR多模态医学图象配准、融合方法,并运用二维9数据图象的Legendre矩正交性和无冗余性的特点,通过找出CT及MR两种模态医学图象的质心,计算出两图象的比例因子,从而完成了两图象的平移和旋转,并精确地实现了CT和MR两模态图象的配信、融合,还优化了Legendre矩的快速算法和提高了应用Legendre矩配准CT和MR图象的速度。实验表明,利用Legendre矩对CT和MR等多模态图象配准、融合,不失为一种比较直接、简洁的方法;同时,Legendre矩在医学影象诊断、放疗计划系统等方面也具有重要的应用价值。     

Automatic surface defect detection for mobile phone screen glass based on machine vision

Defect detection using machine vision technology plays an important role in the manufacturing process of mobile phone screen glass (MPSG). This study proposes an improved detection algorithm for MPSG defect recognition and segmentation. Considering the problem of MPSG image misalignment caused by vibrations in the mobile stages, a contour-based registration (CR) method is used to generate the template image used to align the MPSG images. Based on this registration result, the combination of subtraction and projection (CSP) is used to identify defects on the MPSG image, which can eliminate the influence of fluctuation in ambient illumination. To segment the defects with a fuzzy grey boundary from a noisy MPSG image, an improved fuzzy c-means cluster (IFCM) algorithm is developed in this study. A defect detection system is developed, and the proposed algorithms are validated using a number of experimental tests on MPSG images. The testing results demonstrate that the approach proposed in this study can effectively detect various defects on MPSG and that it has better performance than other methods.     

Behavioural study of median associative memory under true-colour image patterns

Median associative memories (MED-AMs) are a special type of associative memory that substitutes the maximum and minimum operators of a morphological associative memory with the median operator. This associative model has been applied to restore grey scale images and provided a better performance than morphological associative memories when the patterns are altered with mixed noise. Despite their power, MED-AMs have not been adopted in problems related with true-colour patterns. In this paper, we describe how MED-AMs can be applied to problems involving true-colour patterns. Furthermore, a complete study of the behaviour of this associative model in the restoration of true-colour images is performed using a benchmark of 16,000 images altered by different noise types.     

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 ,并能较方便地选择对应点集