眼底OCT图像的公共数据获取及其分析算法

眼底疾病是致盲的主要原因之一。借助光学相干层析成像技术（OCT），可实现早期眼底疾病的发现和及时治疗，是预防失明的有效手段。为缓解医生的阅片压力，计算机辅助诊断技术逐渐受到关注。然而，受限于眼底OCT数据的隐私性，计算机辅助技术的研究者无法获取数据来开展工作。针对该现状，检索梳理了8个免费的公开的眼底OCT数据库，对涉及的典型眼底疾病的OCT图像特征进行解释，并筛选出64篇基于这些数据做计算机辅助算法的文献，分类阐述了这些工作的贡献。为真正实现计算机辅助技术在眼底疾病早期诊断的临床应用，未来还可以从提高眼底OCT图像的高精度分类的稳定性可重复性和泛化能力、提高对眼底OCT图像的分割能力、提高计算机辅助算法的可解释性三方面进行努力。     

眼底图像中黄斑中心与视盘自动检测新方法

在眼底图像自动分析中,视盘与黄斑的定位是糖尿病性视网膜病变计算机辅助诊断或筛查的先决条件。该文提出一种应用方向局部对比度滤波结合局部血管密度的方法,直接先行检测黄斑中心再行定位视盘,不同于现有的先行检测视盘或血管再行定位黄斑的一般方法,有效地提高黄斑定位正确率,能更好地应用于糖尿病性黄斑水肿的自动评估。实验选取了网络公开的HEI-MED数据集中169幅黄斑水肿眼底图像,黄斑和视盘的定位正确率同步达到98.2%,算法简单且无监督,优于现有的方法,具有良好的临床应用前景。     

基于局部傅里叶变换的方向自适应匹配滤波的视网膜血管分割

眼底图像中视网膜血管的结构对眼底疾病的分析和诊断具有重要的意义。针对匹配滤波方法中滤波参数的选取问题,文章提出了一种基于局部傅里叶变换的方向自适应匹配滤波的视网膜血管分割算法。文章算法首先对眼底图像进行预处理;通过分析预处理后的眼底图像中的局部傅里叶变换的能量分布,提取出血管点的主方向,同时利用Harris角点检测方法来校正血管分叉点的主方向;然后对每个像素点进行对应方向的匹配滤波;接着利用可变阈值方法对滤波后的图像进行分割;最后使用面积阈值法消除非血管点和噪声等。文章提出的算法对国际上公开的DRIVE库和STARE库进行了测试,实验结果证明了文中算法能够自适应地获取匹配滤波时的角度。     

人工智能在眼底图像识别方法中的应用

眼底在眼科医学中是重要的研究对象,眼科医生往往通过眼底图像来诊断人眼疾病。为提高眼科医生的眼疾诊断率,研究利用图像处理和人工智能技术对眼底图像进行智能识别的可行性。首先对眼底图像数据运用OpenCV、MATLAB包含的多种图像技术进行有效处理,然后利用TensorFlow建立不同结构不同参数的卷积神经网络,对处理后的眼底图像进行特征提取与训练学习;通过改变层级和加入中心损失函数来提升识别率以优化网络,将眼底图像输入网络即可实现眼底疾病的快速分类同时显示相应的识别概率。选取包括糖尿病、青光眼等八类500张250*166像素眼底图片进行分类训练及测试实验。结果表明,网络模型在迭代次数为20 000次时,识别率达到44. 81%。该方法实现了对眼底图像的识别,可帮助眼科医生辅助判断眼疾。     

基于背景估计和集成分类的眼底硬性渗出检测

眼底图像中硬性渗出的检测对于糖尿病性视网膜病变(简称"糖网")的早期诊断具有重要意义.为了实现眼底图像中硬性渗出物的自动检测,本文提出了一种结合背景估计和集成分类的眼底图像硬性渗出物自动检测方法.首先,对图像进行亮度校正、去噪、对比度增强等预处理操作,然后结合形态学方法进行背景估计和图像重建并移除视盘区域,得到渗出物候选区域.最后利用集成分类方法对候选区域进行分类,提取最终的硬性渗出区域.实验结果表明,本方法能够有效准确地检测到眼底图像中的硬性渗出物,对于糖网自动诊断技术的研究具有积极意义.     

基于计算机图像分析的青光眼诊断系统

介绍了青光眼的计算机诊断系统的硬件组成，青光眼诊断所需要的图像参数以及为获取这些图像参数所进行的图像处理步骤，并提出了从眼底图像参数到不同种类青光眼的人工神经网络诊断法。     

基于卷积运算的视网膜层结构自动分割算法研究

在眼科临床医学中,视网膜的形态结构特征是医生对患者进行眼科疾病诊断的重要依据.基于光学相干层析成像技术(OCT)可以得到完整的视网膜结构图像,但现有的OCT图像分割方法中,散斑噪声和灰度分布不均匀对这些方法的性能产生负面作用,难以满足临床应用的需求.针对上述问题,为了提高医生的诊断效率并降低误诊率,提出了一种自动分割视网膜层结构的算法,通过伽玛变换、中值滤波、二值化、形态学处理、边缘检测、最小二乘法曲线拟合等技术,对OCT视网膜图像进行自动分割来辅助医生诊断.实验结果表明,该视网膜结构自动分割方法与专家人工标记的平均相似率达到95.1％,运行时间10.7 s,具有良好的有效性和实时性,满足临床应用的需求.     

一种无信息丢失的光学相干层析图像校正方法

文章提出一种无信息丢失的图像处理方法,用以校正受到生物组织随机蠕动影响的光学相干层析(OCT)图像.文中首先利用相干增强各向异性扩散算法对原始OCT图像预处理增强层状结构,再利用顺序查找峰值的方法找出某层状结构及其周围层状结构的位置,进而利用异常点去除的方法对该层状结构进行校正,并以校正后得到的该层状结构位置作为基准校正图像.对眼底OCT图像去抖动的实验说明该方法能很好的去除OCT图像中的抖动,从而很好地辅助诊断.     

基于Canny边缘检测和外观特征的微血管瘤检测方法

糖尿病性视网膜病变进行早期筛查可以减少疾病的发展并且阻止随后的视力损害。微血管瘤是糖尿病性视网膜病变的早期临床症状,可以通过微血管瘤检测对糖尿病性视网膜病变进行早期筛查。针对眼底图像中视网膜血管、视盘、渗出物以及微血管瘤之间的相互关系,在红色通道和绿色通道加权图上定位出视盘,在绿色通道上采用基于简单统计的自适应双阈值Canny算子进行边缘检测,并进行封闭区域的填充。设定阈值消除大面积对象并移除视网膜血管、视盘和渗出物得到微血管瘤的候选区域,最后根据形状特征和颜色特征从候选区域中得到真正的视网膜微血管瘤。实验结果表明,该算法能够有效提取视网膜眼底图像中的微血管瘤,敏感性和阳性预测值分别达到92%和86%,优于现有一些典型的微血管瘤检测方法,能够精确地检测出微血管瘤,可用在糖尿病性视网膜病变早期筛查中。     

基于光学相干层析技术的甲状腺乳头状癌图像识别

光学相干层析技术（OCT）作为一种实时、无创的高分辨率成像手段，能够使用特征提取算法获得丰富的图像信息，为疾病的诊断提供客观依据。利用OCT对17例甲状腺正常组织与乳头状癌组织进行成像。针对甲状腺组织图像的特点，使用灰度共生矩阵（GLCM）、灰度直方图（GH）、中心对称自相关（CSAC）和Laws纹理测度（LM）4种算法提取图像特征值，并结合支持向量机（SVM）算法定量地评估不同特征组合的识别性能。结果显示，GLCM-GH-LM组合性能最优，能够从多个方面获得图像的纹理和灰度特征信息，灵敏度、特异性和准确度分别高达96.3%、92.2%和94.3%。研究表明，基于特征提取和机器学习的算法对甲状腺乳头状癌OCT图像进行量化分析及识别时不仅可以提供实时的监测图像，还对甲状腺恶性肿瘤临床诊断具有重要的参考价值。     

Aligning scan acquisition circles in optical coherence tomography images of the retinal nerve fibre layer

Optical coherence tomography (OCT) is widely used in the assessment of retinal nerve fibre layer thickness (RNFLT) in glaucoma. Images are typically acquired with a circular scan around the optic nerve head. Accurate registration of OCT scans is essential for measurement reproducibility and longitudinal examination. This study developed and evaluated a special image registration algorithm to align the location of the OCT scan circles to the vessel features in the retina using probabilistic modelling that was optimised by an expectation-maximization algorithm. Evaluation of the method on 18 patients undergoing large number of scans indicated improved data acquisition and better reproducibility of measured RNFLT when scanning circles were closely matched. The proposed method enables clinicians to consider the RNFLT measurement and its scan circle location on the retina in tandem, reducing RNFLT measurement variability and assisting detection of real change of RNFLT in the longitudinal assessment of glaucoma.     

Automatic segmentation of optic disc and cup for CDR calculation

Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio (CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.     

Optimum hierarchical segmentation of OCT images in retina based on macular distance

Retina layering was the basis of optic disc structure analysis and 3D feature extraction of glaucoma.In order to improve the layering effect of retinal OCT images,an intensity based multilayer segmentation algorithm for two-dimensional retinal OCT images was proposed.Through preprocessing and filtering operation,the segmentation algorithm calculated the intensity and intensity gradient of each A-scan in the retinal OCT image to obtain the upper bound RNFL,the dividing line IS and OS,and the lower bound RPE.Then macular distance strategy,calculated by the shortest distance,was used to further optimize the layering result of macular area,so as to achieve layering segmentation of the retinal OCT images.The experimental results show the algorithm has good optimization effect,low time complexity and fast running speed.     

Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease

Glaucoma is a disease characterized by damaging the optic nerve head, this can result in severe vision loss. An early detection and a good treatment provided by the ophthalmologist are the keys to preventing optic nerve damage and vision loss from glaucoma. Its screening is based on the manual optic cup and disc segmentation to measure the vertical cup to disc ratio (CDR). However, obtaining the regions of interest by the expert ophthalmologist can be difficult and is often a tedious task. In most cases, the unlabeled images are more numerous than the labeled ones.We propose an automatic glaucoma screening approach named Super Pixels for Semi-Supervised Segmentation “SP3S”, which is a semi-supervised superpixel-by-superpixel classification method, consisting of three main steps. The first step has to prepare the labeled and unlabeled data, applying the superpixel method and bringing in an expert for the labeling of superpixels. In the second step, We incorporate prior knowledge of the optic cup and disc by including color and spatial information. In the final step, semi-supervised learning by the Co-forest classifier is trained only with a few number of labeled superpixels and a large number of unlabeled superpixels to generate a robust classifier. For the estimation of the optic cup and disc regions, the active geometric shape model is used to smooth the disc and cup boundary for the calculation of the CDR. The obtained results for glaucoma detection, via an automatic cup and disc segmentation, established a potential solution for glaucoma screening. The SP3S performance shows quantitatively and qualitatively similar correspondence with the expert segmentation, providing an interesting tool for semi-automatic recognition of the optic cup and disc in order to achieve a medical progress of glaucoma disease.     

基于3D打印技术的眼科OCT设备计量校准装置研制

为了评估眼科光学相干断层成像（OCT）设备的分辨率、视场角、图像匹配度、深度测量准确性等多个关键参数,确保设备输出量值的准确性与有效性,设计并研制了一种模拟真实人眼结构且参数可溯源的模拟眼,包含角膜和晶状体等人眼主要屈光结构。设计并依托3D打印技术加工了用于横向与轴向分辨率检测的三维分辨率板;设计加工了用于视场角检测的阶梯状同心圆环结构;同时设计加工了用于图像匹配度检测的交叉光纤组件和用于深度测量准确性参数检测的平行玻璃板组件,可适配于模拟眼眼底凹槽内。使用共焦拉曼显微镜对三维分辨率板尺寸溯源,横向和轴向最小可检测分辨率分别为9.7 μm和5.7 μm;使用尼康投影仪对同心圆环尺寸及光纤直径溯源,最大可检测视场角109.03°以及最小62.5 μm的图像匹配度检测;使用尼康高度计对平行玻璃板中心厚度溯源,其测量不确定度小于5 μm。经过对商用眼科OCT设备测试表明,结合微纳3D打印技术的计量校准用模拟眼具有精度高、集成度高、适用范围广、稳定性强等优点,适用于眼科OCT设备的计量校准。     

基于交叉网络的眼底视神经乳头自动定位

该文研究基于交叉网络的眼底视神经乳头自动定位的新方法。为描述眼底血管网络空间属性,该文提出一种新的概念交叉网络,并给出了交叉网络属性测度。依据眼底组织结构模型构建交叉网络,利用血管网络交叉密度测度,实现眼底图像视神经乳头的自动定位。采用国际通用的STARE,DRIVE眼底图像库以及临床采集图像进行不同图像质量下定位成功率测试,实验结果验证了算法的有效性。同时运算速度较已有算法也有明显提高,可以满足眼科临床检查的需求。     

Automatic diagnosis of multiple fundus lesions based on depth graph neural network

Fundus images are commonly used to capture changes in fundus structures and the severity of fundus lesions, and are the basis for detecting and treating ophthalmic diseases as well as other important diseases. This study proposes an automatic diagnosis method for multiple fundus lesions based on a deep graph neural network (GNN). 2 083 fundus images were collected and annotated to develop and evaluate the performance of the algorithm. First, high-level semantic features of fundus images are extracted using deep convolutional neural networks (CNNs). Then the features are input into the GNN to model the correlation between different lesions by mining and learning the correlation between lesions. Finally, the input and output features of the GNN are fused, and a multi-label classifier is used to complete the automatic diagnosis of fundus lesions. Experimental results show that the method proposed in this study can learn the correlations between lesions to improve the diagnostic performance of the algorithm, achieving better performance than the original ResNet and DenseNet models in both qualitative and quantitative evaluation.     

Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.     

多算法融合的眼底图像渗出液分割

利用眼底图像中渗出液的亮度与边缘特征,文中采用一种多算法融合的渗出液自动检测分割方法来解决目前传统算法灵敏度低以及检测中存在视盘和其它微血管瘤等暗病灶的干扰等问题。为了提高分割效率和准确率,文中对原始图像进行顶帽底帽变换来增强图像对比度,采用GA与KSW熵法相结合的双阈值分割法对眼底图像进行渗出液分割。实验在Kaggle数据库上进行测试,结果显示该算法在像素层统计的SE和阳性预测值PPV分别为83.6%和93.2%,在图像层统计的SE、SP与AC分别为95.2%、86.2%和90.8%。在另一个独立的DIARETDB1数据库上进行测试,获得的结果分别为82.4%、93.3%、93.6%、96.2%和89.9%。与其它算法对比,文中方法可以很好地区分开渗出液与暗病灶,且检测时间短,具有准确性和高效性。     

基于梯度的低对比度X线图像分割方法

在医学骨折诊断领域,X线图像是骨折诊断的重要手段,X线图像中骨骼组织的精确分割为后续的诊断提供了重要依据。为此,文中首先利用Sobel边缘检测算子得到X线图像的梯度图,然后利用灰度图像单目标跟踪算法,提出了一种基于梯度的多目标分割算法,经过三轮跟踪获得了骨骼组织的精确定位,实现了医学骨科X线图像的骨骼分割。最后,将分割结果与边缘检测和阈值方法的分割结果进行了比较。试验结果表明,该方法对低对比度图像能够得到好的分割效果,比较适合医学骨科X线图像的骨骼分割。