基于深度学习和二维高斯拟合的视网膜血管管径测量方法

糖尿病、高血压等疾病会引起视网膜血管的形状发生变化,眼底图像血管分割是疾病定量分析过程中的关键步骤,对临床疾病的分析和诊断具有指导意义。本文提出一种视网膜血管管径自动测量方法。首先,将通道特征图叠加,同时通过使用深度可分离卷积来增加网络深度,将二者引用于全卷积神经网络中对血管网络进行分割;然后在分割的血管网络基础上,利用形态学细化和最小二乘拟合求取血管的中心线和方向;最后根据血管横截面灰度值分布特性,利用二维高斯拟合对血管中心线和方向进行校正,得到准确的血管方向和中心线位置进而计算血管管径。利用本文方法分别对REVIEW数据库中的3个图像集进行测试,测量的管径均值的标准差接近专家测量的标准差,表明本文血管管径测量方法的准确率高,实验结果验证了本文方法的准确性。     

基于中心线提取的视网膜血管分割

精确估计眼底图像中的血管中心线和血管宽度是眼底血管疾病量化和可视化诊断的先决条件。在深入研究眼底图像和视网膜血管特征的基础上,提出一种基于中心线提取的视网膜血管分割算法,该算法以离散高斯核函数的偏微分为模板卷积图像,利用血管的微分几何特征定位血管中心线,并测量血管宽度。本方法的血管分割精度达到亚像素级,解决了传统方法无法直接计算血管宽度的问题,且在速度方面有较大提高。应用本算法对各类眼底图像进行了血管分割的实验,结果表明,该算法可以快速、准确地进行眼底血管分割和中心线提取。     

眼底血管形态计算机分析系统及应用

本文利用计算机图像处理技术和微循环血管网络定量分析技术，开发了一套用于视网膜血管形态参数定量分析的计算机图像处理系统。系统主要包括眼底照相、图像采集、图像显示、处理、参数测量及显示和诊断结果（包括图像和测量结果）打印输出、眼底图谱等几部分。临床应用显示，该系统有助于高血压眼底疾病等疾病的辅助诊断。     

基于拓扑结构的眼底图像分割评价新方法

在眼底图像分割效果的评价中,针对传统评价方法只考虑像素点重合而未考虑视网膜血管拓扑结构的不足,本文提出一个新的评价方法。该方法首先利用数学形态学和细化算法得到血管的拓扑结构,然后统计并分析视网膜血管区域3个特征参数的分布情况,即以互信息、相关系数和节点率来获得基于拓扑结构的眼底图像分割评价结果。该方法的实验数据取自STARE公开数据库中专家手工分割及其腐蚀结果。实验结果表明:互信息、相关系数和节点率这三个特征参数可以从拓扑结构的角度来评价眼底图像视网膜血管的分割效果,且算法复杂程度较低,该方法对眼底图像视网膜血管分割评价方法的补充有重要意义。     

BP神经网络在眼底造影图像分割中的应用

背景:通过眼底荧光血管造影(FFA)所得到的数字图像以及对其进行处理所得到的数据,可反映视网膜血管结构、血流动力学改变、血管病理生理变化及其相关结构的病理改变,广泛应用于视网膜、脉络膜及视神经疾病的鉴别诊断。目的:通过分析眼底造影图像和BP神经网络的特点,利用BP神经网络对眼底造影图像进行分割,并将其利用到眼科的临床辅助诊断之中。方法:将待分割图像区域分为背景和目标两类,用手工方法得到这两类的样本图像,提取样本图像的特征,如灰度、方差、纹理等;对提取的样本特征值进行归一化处理,输入神经网络分类器,利用BP训练算法进行训练;输入待分类的医学图像,提取图像特征,并进行归一化处理;将归一化后的特征值,输入已训练的神经网络分类器进行分类,得到眼底造影图像的分割结果。结果与结论:本文使用的眼底造影图像分割方法抗干扰能力强,分割的眼底造影图像清晰、内容丰富。可以为眼科医生的临床诊断提供较大帮助。     

眼底血管形态计算机分析系统及应用

本文利用计算机图像处理技术和微循环血管网络定量分析技术，开发了一套用于视网膜血管形态参数定量分析的计算机图像处理系统。系统主要包括眼底照相、图像采集、图像显示、处理、参数测量及显示和诊断结果（包括图像和测量洁果）打印输出、眼底图谱等几部分。临床应用显示，该系统有助于高血压眼底病等疾病的辅助诊断。     

基于改进U型网络的眼底图像血管分割

眼底图像血管分割问题是眼科及其他相关疾病计算机辅助诊断的基础。通过分割和分析眼底图像中的血管结构,可以对糖尿病视网膜病变、高血压和动脉硬化等疾病进行早期诊断和监测。针对目前已有血管分割算法存在准确率不高和灵敏度较低的问题,基于深度学习基本理论,提出一种改进U型网络的眼底图像血管分割算法。首先,通过减少传统U型网络下采样和上采样操作次数,解决眼底图像数据较少的问题;其次,通过将传统卷积层串行连接方式改为残差映射相叠加的方式,提高特征的使用效率;最后,在卷积层之间加入批量归一化和PReLU激活函数对网络进行优化,使网络性能得到进一步的提升。在DRIVE和CHASE_DB1这两个公开的眼底数据库上进行实验,每个数据库随机抽取160 000个图像块送入改进的网络中进行训练和测试,可以得到该算法在两个数据库上的灵敏度、准确率和AUC(ROC曲线下的面积)值,相比已有算法的最好结果平均分别提高2.47%、0.21%和0.35%。所提出的算法可改善眼底图像细小血管分割准确率不高及灵敏度较低的问题,能够较好地分割出低对比度的微细血管。     

结合深度可分离卷积与通道加权的全卷积神经网络视网膜图像血管分割

糖尿病和高血压等疾病会引起视网膜血管的形状发生变化,眼底图像血管分割是疾病定量分析过程中的关键步骤,对临床疾病的分析和诊断具有指导意义。本文提出一种结合深度可分离卷积与通道加权的全卷积神经网络(FCN)视网膜图像血管分割方法。首先,对眼底图像的绿色通道进行CLAHE及Gamma校正以增强对比度;然后,为了适应网络训练,对增强后的图像进行分块以扩充数据;最后,以深度可分离卷积代替标准的卷积方式以增加网络宽度,同时引入通道加权模块,以学习的方式显式地建模特征通道的依赖关系,提高特征的可分辨性。将二者结合应用于FCN网络中,以专家手动标识结果作为监督在DRIVE数据库进行实验。结果表明,本文方法在DRIVE库的分割准确性能够达到0.963 0,AUC达到0.983 1,在STARE库的分割准确性可以达到0.962 0,AUC达到0.983 0。在一定程度上,本文方法具有更好的特征分辨性,分割性能较好。     

一种基于高斯匹配滤波法的视网膜图像血管中轴线快速提取算法

目的：研制快速、精确的视网膜血管中轴线（ｖｅｓｓｅｌ ａｘｉａｌ ｓｋｅｌｅｔｏｎ ＶＡＳ）提取算法，以定量分析眼底血管形态改变的细微变化。方法：１．图像预处理（去噪、增强）。２．以经二值化及膨胀后的梯度图像为模板，进行基于高斯匹配滤波法的快速血管提取。３．后处理（细化等）。结果：分别采用本文方法及传统方法对视网膜ＶＡＳ进行提取与结果比较。结论：本文方法具有快速、精确、抗噪声能力强及提取出的ＶＡＳ     

基于Gabor滤波器的糖尿病视网膜新生血管检测

目的:采用Gabor滤波器实现眼底图像中新生血管检测,帮助医生准确确定糖尿病视网膜病变的分期。 方法:对眼底图像进行预处理,并使用不同尺度参数和方向参数Gabor滤波器作用于预处理图像,并在尺度参数确定的情况下取各方向输出结果的最大值作为最后Gabor滤波器的输出。 结果:对比分析不同尺度参数的Gabor滤波器的结果,发现小尺度参数的Gabor滤波器在新生血管部分具有较强的输出。 结论:本研究提出的Gabor滤波器可以很好地区分眼底图像中正常血管与新生血管结构。     

Extraction and reconstruction of retinal vasculature

Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and reconstructing retinal vasculature. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods, especially with low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database show that it outperforms many other published methods and achieved an accuracy range (ability to detect both vessel and non-vessel pixels) of 0.91 – 0.95, a sensitivity range (ability to detect vessel pixels) of 0.91 – 0.95 and a specificity range (ability to detect non-vessel pixels) of 0.88 – 0.94.     

Extraction and reconstruction of retinal vasculature

Information about retinal vasculature morphology is used in grading the severity and progression of diabetic retinopathy. An image analysis system can help ophthalmologists make accurate and efficient diagnoses. This paper presents the development of an image processing algorithm for detecting and reconstructing retinal vasculature. The detection of the vascular structure is achieved by image enhancement using contrast limited adaptive histogram equalization followed by the extraction of the vessels using bottom-hat morphological transformation. For reconstruction of the complete retinal vasculature, a region growing technique based on first-order Gaussian derivative is developed. The technique incorporates both gradient magnitude change and average intensity as the homogeneity criteria that enable the process to adapt to intensity changes and intensity spread over the vasculature region. The reconstruction technique reduces the required number of seeds to near optimal for the region growing process. It also overcomes poor performance of current seed-based methods, especially with low and inconsistent contrast images as normally seen in vasculature regions of fundus images. Simulations of the algorithm on 20 test images from the DRIVE database show that it outperforms many other published methods and achieved an accuracy range (ability to detect both vessel and non-vessel pixels) of 0.91 - 0.95, a sensitivity range (ability to detect vessel pixels) of 0.91 - 0.95 and a specificity range (ability to detect non-vessel pixels) of 0.88 - 0.94.     

基于SQL SERVER2008的眼科电子病历系统的研制

目的：电子病历系统在现代医学信息处理中占有很高的地位,本文针对医院对于眼科病人信息管理的实际需求,开发了一个眼科电子病历系统,以实现对患者的诊疗信息的管理及眼底图像的处理。方法：系统采用SQL SERVER2008作为应用数据库,ActiveX数据对象（Active Data Object简称ADO）的方式连接数据库,以Visual Studioc＋＋6．0作为开发语言,采用SQL语句拼接的方法实现高级查询,采用改进的Back Propagation（BP）神经网络算法进行图像的分割及处理。结果：该眼科电子病历系统包括系统管理、病人信息管理、信息查询及图像管理等四个模块,可实现患者信息的管理、高级查询及统计分析等功能,另外,还可对眼科眼底造影图像进行处理,可手动及自动测量眼底血管管径及不规则病变的面积。结论：通过SQL语句拼接实现对数据库的高级检索,极大地提高了数据库的操作效率。系统给出的眼底血管和病变面积的测量数据,可为眼底病变的诊断、治疗及预后评估等提供依据。总之,该眼科电子病历系统提供了一个操作简便．界面友好的工具,有助于临床诊疗、教学及科研工作。     

A New Blood Vessel Extraction Technique Using Edge Enhancement and Object Classification

Diabetic retinopathy (DR) is increasing progressively pushing the demand of automatic extraction and classification of severity of diseases. Blood vessel extraction from the fundus image is a vital and challenging task. Therefore, this paper presents a new, computationally simple, and automatic method to extract the retinal blood vessel. The proposed method comprises several basic image processing techniques, namely edge enhancement by standard template, noise removal, thresholding, morphological operation, and object classification. The proposed method has been tested on a set of retinal images. The retinal images were collected from the DRIVE database and we have employed robust performance analysis to evaluate the accuracy. The results obtained from this study reveal that the proposed method offers an average accuracy of about 97 %, sensitivity of 99 %, specificity of 86 %, and predictive value of 98 %, which is superior to various well-known techniques.     

Algorithms for automated oximetry along the retinal vascular tree from dual-wavelength fundus images

We present an automated method to perform accurate, rapid, and objective measurement of the blood oxygen saturation over each segment of the retinal vascular hierarchy from dual-wavelength fundus images. Its speed and automation (2 s per entire image versus 20 s per segment for manual methods) enables detailed level-by-level measurements over wider areas. An automated tracing algorithm is used to estimate vessel centerlines, thickness, directions, and locations of landmarks such as bifurcations and crossover points. The hierarchical structure of the vascular network is recovered from the trace fragments and landmarks by a novel algorithm. Optical densities (OD) are measured from vascular segments using the minimum reflected intensities inside and outside the vessel. The OD ratio (ODR=OD600/OD570) bears an inverse relationship to systemic HbO2 saturation (SO2). The sensitivity for detecting saturation change when breathing air versus pure oxygen was calculated from the measurements made on six subjects and was found to be 0.0226 ODR units, which is in good agreement with previous manual measurements by the dual-wavelength technique, indicating the validity of the automation. A fully automated system for retinal vessel oximetry would prove useful to achieve early assessments of risk for progression of disease conditions associated with oxygen utilization.     

基于数学形态学的视网膜血管提取算法

目的 影像中血管的分割与特征提取,对疾病的早期诊断具有重要意义。针对很多视网膜血管提取算法分割精度不高的问题,提出了运用数学形态学中的高帽变换的方法对其进行检测。方法 首先,选取结构元素为“圆盘形”的形态学对图像进行高帽变换,经过高帽变换后的图像平滑了图像的背景,同时增强了血管在图像中的对比度。其次,对变换后的图像利用Otsu＇s自动分割法对图像进行阈值分割得到血管的二值图像。再次,根据血管在图像中的结构信息和几何信息,利用基于连通域度量的方法,设置连通域的“面积”和“长宽比”两个阈值,去除虚假目标。最后,为保持血管的连续性,对图像进行一次膨胀运算,可将断裂的血管连接起来,减小了实验的误差。结果 通过上述步骤实现了对血管的提取。结论 结果表明,本文算法能有效提取视网膜眼底图像的血管网络,有较强的分割精度。     

Automatic segmentation of age-related macular degeneration in retinal fundus images

Every year an increasing number of people are affected by age-related macular degeneration (ARMD). Consequently, vast amount of information is accumulated in medical databases and manual classification of this information is becoming more and more difficult. Therefore, there is an increasing interest in developing automated evaluation methods to follow up the diseases. In this paper, we have presented an automatic method for segmenting the ARMD in retinal fundus images. Previously used direct segmentation techniques, generating unsatisfactory results in some cases, are more complex and costly than our inverse method. This is because of the fact that the texture of unhealthy areas of macula is quite irregular and varies from eye to eye. Therefore, a simple inverse segmentation method is proposed to exploit the homogeneity of healthy areas of the macula rather than unhealthy areas. This method first extracts healthy areas of the macula by employing a simple region growing method. Then, blood vessels are also extracted and classified as healthy regions. In order to produce the final segmented image, the inverse image of the segmented image is generated as unhealthy region of the macula. The performance of the method is examined on various qualities of retinal fundus images. The segmentation method without any user involvement provides over 90% segmentation accuracy. Segmented images with reference invariants are also compared with consecutive images of the same patient to follow up the changes in the disease.     

Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio

Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vasculature. This paper presents a decision support system for detecting and grading HR using morphometric analysis of retinal vasculature, particularly measuring the arteriovenous ratio (AVR) and retinal vessel tortuosity. In the first step, the retinal blood vessels are segmented and classified as arteries and veins. Then, the width of arteries and veins is measured within the region of interest around the optic disk. Next, a new iterative method is proposed to compute the AVR from the caliber measurements of arteries and veins using Parr–Hubbard and Knudtson methods. Moreover, the retinal vessel tortuosity severity index is computed for each image using 14 tortuosity severity metrics. In the end, a hybrid decision support system is proposed for the detection and grading of HR using AVR and tortuosity severity index. Furthermore, we present a new publicly available retinal vessel morphometry (RVM) dataset to evaluate the proposed methodology. The RVM dataset contains 504 retinal images with pixel-level annotations for vessel segmentation, artery/vein classification, and optic disk localization. The image-level labels for vessel tortuosity index and HR grade are also available. The proposed methods of iterative AVR measurement, tortuosity index, and HR grading are evaluated using the new RVM dataset. The results indicate that the proposed method gives superior performance than existing methods. The presented methodology is a novel advancement in automated detection and grading of HR, which can potentially be used as a clinical decision support system.