人眼像差校正仪视网膜微血管像拼接

针对液晶人眼像差校正仪拍摄的小视场视网膜血管图像的拼接算法进行了研究.通过对传统的Harris特征角点算法的改进,解决了该算法在低对比度、小视场视网膜血管图像处理中定位精度较差、需要三次高斯平滑而导致计算量相对较大以及对噪声比较敏感的缺点.然后将配准好的图像进行融合,实现了视网膜血管图像快速、准确的无缝拼接,扩大了视网膜的观测区域,为医务人员的诊治判断提供了更准确的信息.     

视网膜血管网络的边界识别和寻优算法

本文以视网膜血管灰度图象经二值化变换后形成的二值化网络为基础，提出两种助算法：线化无级扩大法和管径增量符号比较法。用以解决血管网络边界识别的确切性关系以及管径自动寻优问题，为消除随机量化误差提供依据。     

医疗红外线热影像应用于辅助临床冠状动脉血管阻塞率分析

红外线数位热影像非常适合显示受测物体曲面所发射红外线辐射之二维温度分佈,因此红外线心脏热影像又被称为冠状动脉热像血管造影。在本研究中,提出了一种数位影像处理之演算法,以定位血栓阻塞或颈缩狭窄之位置。本演算法包含四个连续之处理过程;首先,针对红外线数位热影像之二维温度灰阶像素进行二值化处理,利用多阈值适性分割来区分心脏(背景)或冠状动脉(前景)像素。接著第二处理过程将分割所得原始动脉像素,经过一连串之胡椒状像素去除(pepper-like pixels removing)、外围像素侵蚀(erosion)、与原区域相减(subtraction)、递迴式邻域探访(recursive neighborhood visiting)、动脉轮廓点串链(point linked list)建立以及短边删除等处理之后,提取出冠状动脉之候选轮廓。在第三个处理过程中,由医生自主点选一冠状动脉分支,利用形态学细化(morphological thinning)以及临域分析(neighborhood analysis),以便计算最长之中心轴曲线。在最后一个处理过程中,在沿著有向中心轴曲线上移动,将每一个中心轴像素至最近之左右边动脉血管轮廓相加起来,成为该中心轴像素之可变血管管径,并沿此弯曲之中心轴绘製出矫直哩程管径图,以提供医生有用之狭窄处生理资讯。接著本研究定义一血管阻塞率公式,以区域阻塞率最小值来预估未来动脉阻塞最可能发生之位置。     

基于线算子引导多尺度匹配滤波的视网膜血管分割

视网膜血管的形态分析在糖尿病、高血压等疾病的计算机辅助诊断中具有重要的意义。因此文章提出了一种基于线算子引导多尺度匹配滤波的视网膜血管分割方法。利用线算子检测到的血管方向最佳匹配角,根据血管方向与其横截面方向垂直的关系计算出高斯匹配滤波的旋转角,构建3个不同尺度的滤波器,提取3维匹配滤波特征,结合两个线算子和预处理后的图像灰度,构造6维特征向量,使用SVM对眼底图像像素进行分类。这样在每个尺度下只需计算血管方向最佳匹配角所对应的旋转角下的匹配滤波响应,降低了多尺度匹配特征提取的计算量。通过对DRIVE数据库的眼底图像进行实验,得到平均准确度、灵敏度和特异性分别为0.9424、0.7701和0.9697,对视网膜血管进行了有效的分割。     

基于HESSIAN多尺度因子的视网膜血管分割方法

在医学领域,许多疾病会引起眼底视网膜血管结构和形态的变化,及时对其检测分析可起到疾病预防作用。针对视网膜血管的连续树杈状图像特征,运用对线段结构有良好检测能力的Hessian矩阵进行视网膜血管分割,然而仅使用单尺度难以分割整个血管网络。文章使用一种基于Hessian多尺度因子的分割方法,构造高斯二维核函数引入空间尺度因子,实现视网膜血管的全面分割。仿真结果表明,多尺度因子在正确分割大血管脉络的同时,对微小血管也具有较好的分割能力。     

多分辨率融合输入的U型视网膜血管分割算法

针对视网膜血管拓扑结构不规则、形态复杂和尺度变化多样的特点,该文提出一种多分辨率融合输入的U型网络(MFIU-Net),旨在实现视网膜血管精准分割。设计以多分辨率融合输入为主干的粗略分割网络,生成高分辨率特征。采用改进的ResNeSt代替传统卷积,优化血管分割边界特征;将并行空间激活模块嵌入其中,捕获更多的语义和空间信息。构架另一U型精细分割网络,提高模型的微观表示和识别能力。一是底层采用多尺度密集特征金字塔模块提取血管的多尺度特征信息。二是利用特征自适应模块增强粗、细网络之间的特征融合,抑制不相关的背景噪声。三是设计面向细节的双重损失函数融合,以引导网络专注于学习特征。在眼底数据用于血管提取的数字视网膜图像(DRIVE)、视网膜结构分析(STARE)和儿童心脏与健康研究(CHASE_DB1)上进行实验,其准确率分别为97.00%, 97.47%和97.48%,灵敏度分别为82.73%, 82.86%和83.24%,曲线下的面积(AUC)值分别为98.74%, 98.90%和98.93%。其模型整体性能优于现有算法。     

基于互信息的荧光素眼底血管造影图像序列的自动配准方法

荧光素眼底血管造影技术(FFA)是眼底疾病诊断的金标准,但是造影过程中病人不可避免地转动眼球,造成FFA图像序列中感兴趣区域(例如视网膜血管分支、新生血管)的位置发生变化,给后续的图像定量分析与病情准确评估诊断带来困难。针对上述问题,该文提出一种基于互信息的FFA图像序列配准方法。首先采用多尺度线性滤波方法分割出图像中的血管,并利用图像金字塔对分割后的图像进行下采样,然后利用互信息计算待配准图像与参考图像的相似性,通过进化策略对配准参数进行优化,获得互信息最大时图像的空间变换矩阵,实现FFA图像的配准。采用上述方法,对4位患者共计1039帧FFA图像进行测试,总体配准率达到93%,失败率仅为1%;与常用的配准方法相比,所提方法的配准率、配准速度和鲁棒性等综合性能良好,为FFA影像的定量分析在未来的临床应用奠定了基础。     

血管内皮生长因子在激光诱导视网膜下新生血管形成中的作用及其抗体的影响

目的：探讨血管内皮细胞生长因子（vascular endothial growth factor,VEGF)在激光诱导兔视网膜下新生血管形成中的作用，观察其抗体对新生血管的影响。方法：高强度氪激光光凝有色家兔视网膜1、3、7、14d后，制备眼球冰冻切片检查眼底病理变化，用组织原位杂交法观察VEGFmRNA表达时相和部位的变化。VEGF抗体治疗组2只眼于光凝后10min玻璃体内注射5μgVEGF抗体，14d后行眼底荧光造影并与对侧眼对照观察荧光渗漏变化。结果（1）正常对照家兔视网膜中未见VEGFmRNA的表达；（2）激光光凝后1-3d，邻近伤口的视网膜节细胞层和内核层VEGFmRNA表达明显增加；（3）光凝后3d，光凝斑附近的视网膜下有新生血管样结构出现；（4）VEGF抗体治疗组荧光染料渗漏程度明显降低。结论：VEGFmRNA的在激光光凝后的视网膜中表达增多，且与光凝斑周围的新生血管化存在时空对应关系，提示VEGF在视网膜下新生血管形成过程中具有重要的作用。VEGF抗体对新生血管形成具有明显的抑制作用。     

病变视网膜图像血管网络的自动分割

现有的视网膜血管分割方法大多只针对正常的视网膜图像进行分割,不能实现对发生病变的视网膜图像的分割.为此,提出了一种新的病变视网膜图像血管网络分割方法.该方法首先采用向量场散度方法获得病变视网膜图像中大部分血管的中心线,然后计算出中心线上各像素点的方向信息并采用改进的定向局部对比度方法检测出中心线两侧的血管像素,最后对获得的血管段末端进行反向外推追踪,分割出最终的血管网络.通过对通用的STARE眼底图像库中所有病变视网膜图像的实验仿真,结果表明本文算法获得了0.9426的ROC曲线面积和0.9502的准确率,算法性能明显优于Hoover算法和Benson等提出的算法.此外,本文算法还克服了Benson算法的局限性,对不同类型的病变视网膜图像都具有较好的鲁棒性.     

基于匹配滤波引导局部特征空间仿射传播聚类的视网膜血管分割

视网膜血管自动分割及形态分析对眼部相关疾病的诊断和筛查具有重要意义。文章提出基于匹配滤波引导局部特征空间仿射传播聚类的视网膜血管分割方法,构造基于组合线性检测器、Hessain最大本征值、Gabor滤波和B-COSFIRE滤波四维特征,每个像素可由四维局部特征向量表示。然后采用匹配滤波引导选取样本像素点构成小样本集,在小样本集中进行局部特征空间的仿射传播聚类,得到血管类和背景类两个聚类中心。对眼底所有像素点与血管类和背景类两个聚类中心的特征空间距离采用最近邻方法进行类别划分,实现对视网膜血管的自动分类。实验数据来源于DRIVE眼底图像库,验证了本文方法对于细小血管及血管分叉和交叉处的性能有较大的提升,改善了提取血管的完整性和连续性。     

Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs

A decreased ratio of the width of retinal arteries to veins [arteriolar-to-venular diameter ratio (AVR)], is well established as predictive of cerebral atrophy, stroke and other cardiovascular events in adults. Tortuous and dilated arteries and veins, as well as decreased AVR are also markers for plus disease in retinopathy of prematurity. This work presents an automated method to estimate the AVR in retinal color images by detecting the location of the optic disc, determining an appropriate region of interest (ROI), classifying vessels as arteries or veins, estimating vessel widths, and calculating the AVR. After vessel segmentation and vessel width determination, the optic disc is located and the system eliminates all vessels outside the AVR measurement ROI. A skeletonization operation is applied to the remaining vessels after which vessel crossings and bifurcation points are removed, leaving a set of vessel segments consisting of only vessel centerline pixels. Features are extracted from each centerline pixel in order to assign these a soft label indicating the likelihood that the pixel is part of a vein. As all centerline pixels in a connected vessel segment should be the same type, the median soft label is assigned to each centerline pixel in the segment. Next, artery vein pairs are matched using an iterative algorithm, and the widths of the vessels are used to calculate the AVR. We trained and tested the algorithm on a set of 65 high resolution digital color fundus photographs using a reference standard that indicates for each major vessel in the image whether it is an artery or vein. We compared the AVR values produced by our system with those determined by a semi-automated reference system. We obtained a mean unsigned error of 0.06 (SD 0.04) in 40 images with a mean AVR of 0.67. A second observer using the semi-automated system obtained the same mean unsigned error of 0.06 (SD 0.05) on the set of images with a mean AVR of 0.66. The testing data and reference standard used in this study has been made publicly available.     

Automatic grading of retinal vessel caliber

New clinical studies suggest that narrowing of the retinal blood vessels may be an early indicator of cardiovascular diseases. One measure to quantify the severity of retinal arteriolar narrowing is the arteriolar-to-venular diameter ratio (AVR). The manual computation of AVR is a tedious process involving repeated measurements of the diameters of all arterioles and venules in the retinal images by human graders. Consistency and reproducibility are concerns. To facilitate large-scale clinical use in the general population, it is essential to have a precise, efficient and automatic system to compute this AVR. This paper describes a new approach to obtain AVR. The starting points of vessels are detected using a matched Gaussian filter. The detected vessels are traced with the help of a combined Kalman filter and Gaussian filter. A modified Gaussian model that takes into account the central light reflection of arterioles is proposed to describe the vessel profile. The width of a vessel is obtained by data fitting. Experimental results indicate a 97.1% success rate in the identification of vessel starting points, and a 99.2% success rate in the tracking of retinal vessels. The accuracy of the AVR computation is well within the acceptable range of deviation among the human graders, with a mean relative AVR error of 4.4%. The system has interested clinical research groups worldwide and will be tested in clinical studies.     

Light paths in retinal vessel oxymetry

The oxygen utilization and, therefore, the metabolic state, of a distinctive area of the retina may be calculated from the diameter of the supplying artery and vein, the haemoglobin oxygenation, and the velocity of the blood. The first two parameters can be determined by imaging spectrometry at the patients ocular fundus. However, the reflected light emerging from a vessel followed different pathways through the ocular fundus layers and the vessel embedded in the retina. The contribution of the single pathways to the vessel reflection profile is investigated by a Monte Carlo simulation. Considering retinal vessels with diameters of 25-200 microm we found the reflection from a thin vessel to be determined by the single and double transmission of light at 560 nm. The backscattering from the blood column determines the reflectance in the case of a thick vessel. However, both components are in the same order of magnitude. This has to be considered in the calculation of the oxygen saturation of blood in retinal vessels from their reflection spectra.     

基于标记分水岭的视频帧图像分割

为了提取血管扩张收缩的变化趋势，在采集原始视频AVI图像，进行帧序列转换后，提取血管轮廓成为研究的重点。文中提出了一种基于标记分水岭对血管超声帧图像的分割方法，该法针对超声图像对比度低、噪声强的特点在标记之前进行小波域增强。实验证明该法较传统的区域生长法更能准确地提取目标轮廓。基于该法得到的血管在连续时间内截面积变化曲线，更方便医师进行精确的病理分析。由此可见，该法更适用于超声血管图像的边界提取。     

Adaptive approach to accurate analysis of small-diameter vessels incineangiograms

In coronary vessels smaller than 1 mm in diameter, it is difficult to accurately identify lumen borders using existing border detection techniques. Computer-detected diameters of small coronary vessels are often severely overestimated due to the influence of the imaging system point spread function and the use of an edge operator designed for a broad range of diameter vessel sizes. Computer-detected diameters may be corrected if a calibration curve for the X-ray system is available. Unfortunately, the performance of this postprocessing diameter correction approach is severely limited by the presence of image noise. The authors report here a new approach that uses a two-stage adaption of edge operator parameters to optimally match the edge operator to the local lumen diameter. In the first stage, approximate lumen diameters are detected using a single edge operator in a half-resolution image. Depending on the approximate lumen size, one of three edge operators is selected for the second full-resolution stage in which left and right coronary borders are simultaneously identified. The method was tested in a set of 72 segments of nine angiographic phantom vessels with diameters ranging from 0.46 to 4.14 mm and in 82 clinical coronary angiograms. Performance of the adaptive simultaneous border detection method was compared to that of a conventional border detection method and to that of a postprocessing diameter correction border detection method. Adaptive border detection yielded significantly improved accuracy in small phantom vessels and across all vessel sizes in comparison to the conventional and postprocessing diameter correction methods (p<0.001 in all cases). Adaptive simultaneous coronary border detection provides both accurate and robust quantitative analysis of coronary vessels of all sizes     

Artery-vein separation via MRA--an image processing approach

This paper presents a near-automatic process for separating vessels from background and other clutter as well as for separating arteries and veins in contrast-enhanced magnetic resonance angiographic (CE-MRA) image data, and an optimal method for three-dimensional visualization of vascular structures. The separation process utilizes fuzzy connected object delineation principles and algorithms. The first step of this separation process is the segmentation of the entire vessel structure from the background and other clutter via absolute fuzzy connectedness. The second step is to separate artery from vein within this entire vessel structure via iterative relative fuzzy connectedness. After seed voxels are specified inside artery and vein in the CE-MRA image, the small regions of the bigger aspects of artery and vein are separated in the initial iterations, and further detailed aspects of artery and vein are included in later iterations. At each iteration, the artery and vein compete among themselves to grab membership of each voxel in the vessel structure based on the relative strength of connectedness of the voxel in the artery and vein. This approach has been implemented in a software package for routine use in a clinical setting and tested on 133 CE-MRA studies of the pelvic region and two studies of the carotid system from six different hospitals. In all studies, unified parameter settings produced correct artery-vein separation. When compared with manual segmentation/separation, our algorithms were able to separate higher order branches, and therefore produced vastly more details in the segmented vascular structure. The total operator and computer time taken per study is on the average about 4.5 min. To date, this technique seems to be the only image processing approach that can be routinely applied for artery and vein separation.     

Real-time control of angioplasty balloon inflation based on feedback from intravascular optical coherence tomography: preliminary study on an artery phantom

A method is proposed to achieve computerized control of angioplasty balloon inflation, based on feedback from intravascular optical coherence tomography (IVOCT). Controlled balloon inflation could benefit clinical applications, cardiovascular research, and medical device industry. The proposed method was experimentally tested for balloon inflation within an artery phantom. During balloon inflation, luminal contour of the phantom was extracted from IVOCT images in real time. Luminal diameter was estimated from the obtained contour and was used in a feedback loop. Based on the estimated actual diameter and a target diameter, a computer controlled a programmable syringe pump to deliver or withdraw liquid in order to achieve the target diameter. The performance of the control method was investigated under different conditions, e.g., various flow rates and various target diameters. The results were satisfactory, as the control method provided convergence to the target diameters in various experiments.     

Quantifying 3-D vascular structures in MRA images using hybrid PDE and geometric deformable models

The aim of this paper is to present a hybrid approach to accurate quantification of vascular structures from magnetic resonance angiography (MRA) images using level set methods and deformable geometric models constructed with 3-D Delaunay triangulation. Multiple scale filtering based on the analysis of local intensity structure using the Hessian matrix is used to effectively enhance vessel structures with various diameters. The level set method is then applied to automatically segment vessels enhanced by the filtering with a speed function derived from enhanced MRA images. Since the goal of this paper is to obtain highly accurate vessel borders, suitable for use in fluid flow simulations, in a subsequent step, the vessel surface determined by the level set method is triangulated using 3-D Delaunay triangulation and the resulting surface is used as a parametric deformable model. Energy minimization is then performed within a variational setting with a first-order internal energy; the external energy is derived from 3-D image gradients. Using the proposed method, vessels are accurately segmented from MRA data.     

Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding

A method for measuring the spatial concentration of specific categories of vessels in a vascular network consisting of vessels of several diameters, lengths, and orientations is demonstrated. It is shown that a combination of the mathematical morphology operation, opening, with a linear rotating structuring element (ROSE) and dual feature thresholding can semi-automatically segment categories of vessels in a vascular network. Capillaries and larger vessels (arterioles and venules) are segmented here in order to assess their spatial concentrations. The ROSE algorithm generates the initial segmentation, and dual feature thresholding provides a means of eliminating the nonedge artifact pixels. The subsequent gray-scale histogram of only the edge pixels yields the correct segmentation threshold value. This image processing strategy is demonstrated on micrographs of vascular casts. By adjusting the structuring element and rotation angles, it could be applied to other network structures where a segmentation by network component categories is advantageous, but where the objects can have any orientation.