C臂X光图像几何失真校正与误差分析

在分析C臂X光图像几何失真原因及表现形式的基础上,综合现有校正方法,实现了一种基于校正模板的图像校正方法.设计并试验了C臂图像几何失真误差分布的评价方案.根据试验结果,分析了图像失真变化规律,给出了不同C臂位姿下计算的校正矩阵的临床适用范围.最后,将本方法应用于机器人辅助骨科临床手术,验证了方法的正确性和有效性.     

基于多项式拟合的C臂投影全局校正法

针对C臂影像增强器采集的投影图像因失真变形而无法直接用于计算机辅助手术的问题,提出了一种基于多项式拟合的C臂投影全局校正法.该方法统一考虑C臂投影图像的3种类型的失真--针垫失真、S型扭曲、图像偏移,利用N阶多项式拟合图像的复合失真,然后利用最小二乘法求解最优化校正系数,从而具有所需标记点数量较少,校正区域连续,步骤简单,易于在线使用的优点.校正误差实验表明:该方法在3阶和4阶多项式的情况下所有标记点最大误差小于0.5像素,误差均方根小于0.26像素,并且对不同姿态的C臂投影图像的校正结果具有稳定性.该方法可用于计算机辅助手术导航、器械定位和术中X光三维锥束重建.     

基于视觉模型的C形臂投影图像快速校正方法

针对普通C形臂投影图像失真影响计算机辅助手术的精度和传统校正方法费时的问题,提出了一种基于摄像机视觉模型的方法来快速校正C形臂X射线投影失真图像。该方法通过分析C形臂X射线投影图像失真的来源和类型,把C形臂系统标定和投影失真图像校正融为一体,再利用视觉模型的Tsai法对其进行标定获取畸变参数,然后利用畸变参数对失真图像进行几何校正。实验结果表明,在放射源到探测器的距离为120cm时,最大误差为8.8个像素,放射源到探测器的距离为121cm时,最大误差为9.1个像素。放射源到探测器的距离变化18mm时,标定获得放射源到探测器的距离变化值为18.11mm,相差0.11mm,并且在不同姿态时C形臂投影失真图像校正结果具有稳定性。该方法的优点是减小了建立理想图像带来的误差,而且步骤简单,容易在线使用。     

光学定位跟踪技术在X线图像导航手术中的研究

近几年来,移动式C形臂X光实时成像系统逐渐在计算机辅助骨科手术导航中获得了普遍的应用.本文采用光学立体定位技术对C型臂在运动时进行实时跟踪,并采用多项式算法对输出的X光图像进行几何校正.另外,在实验室中搭建了基于X线图像导航的骨科手术系统,通过光学定位跟踪系统实时而精确地确定手术器械、图像解剖信息、内植入物之间的坐标关系,获得了满意的手术效果.     

CD-AFM扫描图像的三维拼接方法

基于原子力显微镜(atomic force microscope,AFM)的关键尺寸(critical dimension,CD)测量技术可有效测量MEMS结构的侧壁形貌和线宽,针对CD-AFM的关键共性技术之一提出了一种三维图像拼接方法,旨在把结构正表面图像和侧壁图像拼接成为一幅完整的三维图像.通过旋转样品的方式,利用AFM扫描结构形貌,分别得到其正表面和侧壁的扫描图像.在两幅图像的重叠区域进行图像预处理和快速图像相关匹配,可准确获取图像匹配点.随后,对侧壁扫描图像进行逐列翻转、切割、旋转和拼接等操作,最终可得到结构的三维形貌图像.采用C++语言编写的算法对AFM获得的实际扫描图像进行了三维拼接,拼缝边缘曲线相似程度达到97.62%,结果表明该算法具有很好的准确度.     

使用雅可比行列式的立体像对校正方法

为了快速、准确地寻找立体像对的对应点,通常要将像对进行校正,以消除垂直视差,提高搜索速度和匹配精度.提出了基于雅可比行列式的立体像对校正方法,使用雅各比行列式对图像校正后的失真进行分析,更好地保持源图像采样.通过对合成图像及真实图像的校正实验,表明所提出方法有效保护了图像的采样,并使图像形变较小.     

印品检测过程中基于SIFT 算法缩小匹配范围的方法

针对多个CCD 采集多幅图像会产生一定的重叠区域,为了实现印品在线检测的要求,提出了一种结合SIFT 和缩小匹配范围的图像检测方法。该方法基于SIFT 提取特征点,改进了局部搜索范围,利用RANSAC 算法计算图像坐标变换矩阵,采用多分辨率融合方法对拼接图像进行融合处理。结果表明,采用该方法可以减少匹配和图像检验时间,降低估算概率,完成检测图像拼接。     

一种用于图像拼接的角点匹配算法

针对基于自适应光学技术的人眼眼底待拼接图像灰度、清晰度不一致以及细节的差异使检测出的角点无法匹配,进而导致图像不能拼接的问题,提出一种在OpenCV配置环境下角点检测的基础上,进行角点匹配的新方法.该方法利用角点间的距离和斜率在眼底很小的区域内基本不变的特性,将两幅图像的角点对相互匹配,进而找到两幅图像中三对一一对应的角点.实验结果表明,用该方法可以解决人眼图像拼接的角点检测和角点匹配问题,而且运算速度快,检测效率高.     

基于三角形几何相似性的图像配准与拼接

介绍了一种基于三角形几何相似性的图像配准方法.提取两幅待拼接图像的特征点,将每幅图像各自的重叠区域内或图像内容复杂情况下的选定区域内的特征点任意组合为三角形,得到分别对应于每一幅图像的三角形集合.然后根据定义的新的三角形表示方法,包括最大角方向和最小角方向,在两组三角形集合内层层筛选任意组合的三角形对,最终找到其中的匹配三角形对,即相似三角形对,从而找到匹配的点对.最后计算图像间变换矩阵,对图像进行拼接,得到了一张具有更宽视野的无缝拼接图.该方法只与特征点间相互几何位置有关,所以对两幅图像间的灰度差异、任意的旋转、缩放等都表现了很强的鲁棒性.     

基于特征点的图像拼接算法

通过对常用图像拼接算法的研究,提出一种基于图像特征点的拼接算法,利用梯度方向特征点的数据,确定一组最合理的特征匹配,利用这一数据给出两幅图像间矩阵变换的初值,再利用迭代的方法校正,最终得到精确值,通过仿真结果验证算法的有效性。     

闪光CCD图像的中值-非线性扩散滤波

根据闪光CCD图像的特点,提出了一种中值-非线性扩散滤波(Median-NonlinearDiffusionFiltering,简称MNDF)方法。该方法采用中值预滤波来估计图像的真实边缘,通过求解偏微分方程(PartialDifferentialEquation,简称PDE)来进行非线性扩散滤波,充分发挥了中值滤波和非线性扩散滤波的优势,能更好地消除噪声、保护边缘。实验结果表明,在高斯噪声和脉冲噪声同时存在的情况下,MNDF方法取得的滤波效果较P-M方案和Catte方案要好,信噪比改善因子提高3~5倍,均方误差减小1.3~2.7倍。对闪光照相CCD图像取得了很好的消噪声结果,保护了边缘信息。     

基于直线检测的条码图像几何校正研究

CCD相机采集的图像会产生一定程度的几何变形,需要进行几何校正。常用的图像几何校正方法不适合条码图像的校正,因此,结合已有的边缘检测算法和直线提取算法,提出了基于直线检测的条码图像的几何校正算法。基于直线检测的条码图像校正方法是在已除去背景的图像中,寻找目标的条形码边缘对应的直线,以此确定其偏移角度,并进行扭转校正。实验结果表明,有偏转角度的条码图像运用了所提方法校正后,都得到了有效的几何校正。     

An Efficient Bar Code Image Recognition Algorithm for Sorting System

In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify the source image directly. Finally, the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image. The results of multitype image experiments show that the proposed method is improved by 5+ times computational efficiency compared with the former methods, and can recognize fuzzy images better.     

A simple auto prescan calibration method for multislice fast spin echo MRI

The image quality of fast spin echo (FSE) is more sensitive than the typical spin echo pulse sequence caused by the eddy current effect. Microsecond‐scale misalignment of primary spin echoes produces a large spatial variation in image signal intensity. In this study, we describe an auto prescan calibration method that can improve the FSE image quality and minimize the eddy current effect on the image. We used a 0.32 T MRI system and obtained phantom and lumbar images. For FSE image correction, the optimal ranges and steps were determined to find the appropriate values, which were added to or subtracted from the gradient area values for each slice. The appropriate value of each slice could be found using the maximum signal intensity when the refocusing gradient area was changed by a number of steps in the optimal range. The determined value of each slice was applied before each slice image acquisition. The determined optimal step numbers and ranges were applied to in vivo image acquisition, and confirmed the reconstructed image quality. Based on our results, the obtained phantom and lumbar images were shown to be well corrected. The corrected images represented the image quality improvement and elimination of ghosting and blurring artifacts. In conclusion, we have proposed an FSE correction technique that automatically adjusts slice selection for the refocusing gradient balance during prescan, and confirmed that the calibration technique is very reliable even within complex in vivo images. We believe that our proposed technique will greatly benefit in MRI systems. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 289–293, 2013     

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients’ data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.     

一种高通量dPCR荧光图像自适应增强算法

目的 为了改善荧光图像背景光照不均匀和对比度低的问题,提出一种荧光图像自适应亮度校正和低对比度增强算法。方法 根据光照成像原理,利用引导滤波提取出荧光图像的光照分量,通过改进的二维Gamma函数动态校正背景光照,利用Top-hat变换分离出校正后的前景和背景,对前景进行自适应直方图均衡化,以实现荧光图像自适应增强的目的。结果 对比传统算法,文中算法处理后的图像背景光照均匀,对比度增强效果明显,其中标准差平均提高了9.4倍,平均梯度平均提高了1.2倍,信息熵平均提高了0.2倍。结论 文中算法可以改善高通量dPCR荧光图像背景光照不均匀性,提高图像对比度,突出图像中隐藏的细节,对其他荧光图像处理也具有参考价值。     

A new approach to medical image stitching using minimum average correlation energy filter and peak to side‐lobe ratio

Standard X‐ray images using conventional screen‐film technique have a limited field of view and failed to visualize the entire long bone on a single image. To produce images with whole body parts, digitized images from the films that contain portions of the body parts are assembled using image stitching. This article presents a new medical image stitching method that uses minimum average correlation energy filters to identify and merge pairs of X‐ray medical images. The effectiveness of the proposed method is demonstrated in the experiments involving two databases that contain a total of 40 pairs of overlapping and nonoverlapping images. Then the experimental results are compared to those of the normalized cross correlation (NCC) method. It is found that the proposed method outperforms the NCC method in identifying both the overlapping and nonoverlapping medical images. The efficacy of the proposed method is further vindicated by its average execution time which is about five times shorter than that of the NCC method. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 166–171, 2012     

基于意象重组的展示设施设计方法

目的 构建一种基于意象重组的展示设施设计方法，为相关设计提供参考。方法 首先，对不同意象在设计过程中的作用进行分析，并对意象进行分类，厘清不同意象在设计过程中的转化过程。然后，参照生物DNA重组理论，从同源性意象和非同源性意象维度出发，构建意象重组方法模型和基于意象重组的设计方法模型，并阐释意象重组的方法及其应用过程。最后，结合设计实例，阐释该方法的操作过程。结果 通过设计实例验证了该方法具有良好的可操作性，并且能够提升设计效果。结论 在设计输出过程中，对不同意象进行分析可知，非同源性意象提供设计灵感来源的最初原型，同源性意象提供设计表现手段的参考与指导，两者相互结合使模糊的设计概念逐步具象化，为设计目标提供了方向和引导。     

Multivariate analysis-based image enhancement model for machine vision inspection

Image enhancement is an essential procedure in machine vision-based inspection. In practical applications, image enhancement is usually a part of image pre-processing, intended to make the following inspection more effective. The image enhancement method is usually selected by trial-and-error or on the basis of experience. This paper presents an automatic procedure for fast and effective image enhancement. The procedure uses multivariate analysis to automatically construct an optimal image enhancement model. First, an optimally enhanced image was selected from the literature as a basis for the model. Then, the image features were identified and Wilks’ statistic was used for feature selection. Next, discriminate functions were built to select the optimal image enhancement method. To verify the model, 53 training images from the literature and 12 test images from a local company were used in an experimental analysis. The model achieved 98.11% accuracy in selecting the most suitable image enhancement method, and the average increase in contrast was 98% for the 53 training images. The enhancement method selection results for the 12 test images were also in agreement with the 53 training images from the literature. The results show that the proposed method is effective and appropriate for quickly improving image contrast.