Construction and verification of retinal vessel segmentation algorithm for color fundus image under BP neural network model

To improve the accuracy of retinal vessel segmentation, a retinal vessel segmentation algorithm for color fundus images based on back-propagation (BP) neural network is proposed according to the characteristics of retinal blood vessels. Four kinds of green channel image enhancement results of adaptive histogram equalization, morphological processing, Gaussian matched filtering, and Hessian matrix filtering are used to form feature vectors. The BP neural network is input to segment blood vessels. Experiments on the color fundus image libraries DRIVE and STARE show that this algorithm can obtain complete retinal blood vessel segmentation as well as connected vessel stems and terminals. When segmenting most small blood vessels, the average accuracy on the DRIVE library reaches 0.9477, and the average accuracy on the STARE library reaches 0.9498, which has a good segmentation effect. Through verification, the algorithm is feasible and effective for blood vessel segmentation of color fundus images and can detect more capillaries.

     

Retinal vessels segmentation based on level set and region growing

Retinal vessels play an important role in the diagnostic procedure of retinopathy. Accurate segmentation of retinal vessels is crucial for pathological analysis. In this paper, we propose a new retinal vessel segmentation method based on level set and region growing. Firstly, a retinal vessel image is preprocessed by the contrast-limited adaptive histogram equalization and a 2D Gabor wavelet to enhance the vessels. Then, an anisotropic diffusion filter is used to smooth the image and preserve vessel boundaries. Finally, the region growing method and a region-based active contour model with level set implementation are applied to extract retinal vessels, and their results are combined to achieve the final segmentation. Comparisons are conducted on the publicly available DRIVE and STARE databases using three different measurements. Experimental results show that the proposed method reaches an average accuracy of 94.77% on the DRIVE database and 95.09% on the STARE database.     

改进的形态学与Otsu相结合的视网膜血管分割

针对视网膜图像采集过程中由于疾病引起的图像光照反射过强问题,提出了一种修正的形态学与Otsu相结合的无监督视网膜血管分割算法。首先运用形态学中的高低帽变换增强血管与背景的对比度;然后提出了一种修正方法,消除部分由视网膜疾病引起的光照问题;最后使用Otsu阈值方法分割血管。算法在DRIVE和STARE视网膜图像数据库中进行了测试,实验结果表明,DRIVE数据库中的分割精度为0.9382,STARE数据库中的分割精度为0.9460,算法的执行时间为1.6s。算法能够精确地分割出视网膜血管,与传统的无监督视网膜血管分割算法相比,算法的分割精度高、抗干扰能力强。     

基于密集注意力网络的视网膜血管图像分割

视网膜血管的结构信息对眼科疾病的诊断具有重要的指导意义,对视网膜血管图像进行高效正确的分割成为临床的迫切需求。传统的人工分割方法耗时较长且易受个人主观因素的影响,分割质量不高。为此,提出一种基于密集注意力网络的图像自动分割算法。将编码器-解码器全卷积神经网络的基础结构与密集连接网络相结合,以充分提取每一层的特征,在网络的解码器端引入注意力门模块,对不必要的特征进行抑制,提高视网膜血管图像的分割精度。在DRIVE和STARE眼底图像数据集上的实验结果表明,与其他基于深度学习的算法相比,该算法的敏感性、特异性、准确率和AUC值均较高,分割效果较好。     

DF-Net: Deep fusion network for multi-source vessel segmentation

Accurate retinal vessel segmentation is very challenging. Recently, the deep learning based method has greatly improved performance. However, the non-vascular structures usually harm the performance and some low contrast small vessels are hard to be detected after several down-sampling operations. To solve these problems, we design a deep fusion network (DF-Net) including multiscale fusion, feature fusion and classifier fusion for multi-source vessel image segmentation. The multiscale fusion module allows the network to detect blood vessels with different scales. The feature fusion module fuses deep features with vessel responses extracted from a Frangi filter to obtain a compact yet domain invariant feature representation. The classifier fusion module provides the network more supervision. DF-Net also predicts the parameter of the Frangi filter to avoid manually picking the best parameters. The learned Frangi filter enhances the feature map of the multiscale network and restores the edge information loss caused by down-sampling operations. The proposed end-to-end network is easy to train and the inference time for one image is 41ms on a GPU. The model outperforms state-of-the-art methods and achieves the accuracy of 96.14%, 97.04%, 98.02% from three publicly available fundus image datasets DRIVE, STARE, CHASEDB1, respectively. The code is available at https://github.com/y406539259/DF-Net.     

Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation

Automatic extraction of blood vessels is an important step in computer-aided diagnosis in ophthalmology. The blood vessels have different widths, orientations, and structures. Therefore, the extracting of the proper feature vector is a critical step especially in the classifier-based vessel segmentation methods. In this paper, a new multi-scale rotation-invariant local binary pattern operator is employed to extract efficient feature vector for different types of vessels in the retinal images. To estimate the vesselness value of each pixel, the obtained multi-scale feature vector is applied to an adaptive neuro-fuzzy inference system. Then by applying proper top-hat transform, thresholding, and length filtering, the thick and thin vessels are highlighted separately. The performance of the proposed method is measured on the publicly available DRIVE and STARE databases. The average accuracy 0.942 along with true positive rate (TPR) 0.752 and false positive rate (FPR) 0.041 is very close to the manual segmentation rates obtained by the second observer. The proposed method is also compared with several state-of-the-art methods. The proposed method shows higher average TPR in the same range of FPR and accuracy.

     

基于双注意力编码-解码器架构的视网膜血管分割

眼底视网膜血管的分割提取对于糖尿病、视网膜病、青光眼等眼科疾病的诊断具有重要的意义。针对视网膜血管图像中的血管难以提取、数据量较少等问题,文中提出了一种结合注意力模块和编码-解码器结构的视网膜血管分割方法。首先对编码-解码器卷积神经网络的每个卷积层添加空间和通道注意力模块,加强模型对图像特征的空间信息和通道信息(如血管的大小、形态和连通性等特点)的利用,从而改善视网膜血管的分割效果。其中,空间注意力模块关注于血管的拓扑结构特性,而通道注意力模块关注于血管像素点的正确分类。此外,在训练过程中采用Dice损失函数解决了视网膜血管图像正负样本不均衡的问题。在3个公开的眼底图像数据库DRIVE,STARE和CHASE_DB1上进行了实验,实验数据表明,所提算法的准确率、灵敏度、特异性和AUC值均优于已有的视网膜血管分割方法,其AUC值分别为0.9889,0.9812和0.9831。实验证明,所提算法能够有效提取健康视网膜图像和病变视网膜图像中的血管网络,能够较好地分割细小血管。     

Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks

In medicine, diagnosis is as important as treatment. Retinal blood vessels are the most easily visible vessels in the whole body, and therefore, play a key role in the diagnosis of numerous diseases and eye disorders. Systematic and eye diseases cause morphologic variations, such as the growing, narrowing or branching of retinal blood vessels. Imaging-based screening of retinal blood vessels plays an important role in the identification and follow-up of eye diseases. Therefore, automatic retinal vessel segmentation can be used to diagnose and monitor those diseases. Computer-aided algorithms are required for the analysis of progression of eye diseases. This study proposes a hybrid method that provides a combination of pre-processing and data augmentation methods with a deep learning model. Pre-processing was used to solve the irregular clarification problems and to form a contrast between the background and retinal blood vessels. After pre-processing step, a convolutional neural network (CNN) was designed and then trained for the extraction of retinal blood vessels. In the training phase, data augmentation was performed to improve training performance. The CNN was trained and tested in the DRIVE database, which is commonly used in retinal blood vessel segmentation and publicly available for studies in this area. Results showed that the proposed system extracted vessels with a sensitivity of 77.78%, specificity of 97,84%, precision of 84.17% and accuracy of 95.27%.

This study also compared the results to those of previous studies. The comparison showed that the proposed method is an efficient and successful method for extracting retinal blood vessels. Moreover, the pre-processing phases improved the system performance. We believe that the proposed method and results will make contribution to the literature.

     

基于多尺度2D Gabor小波的视网膜血管自动分割

眼底视网膜血管分割对临床视网膜疾病诊断具有重要意义. 由于视网膜血管结构微小, 血管轮廓边界模糊, 加上图像采集时噪声的影响, 视网膜血管分割非常困难. 本文提出一种视网膜血管自动分割新方法. 首先, 应用对比度受限的自适应直方图均衡法增强视网膜图像;然后, 采用不同尺度的2D Gabor小波对视网膜图像进行变换, 并分别应用形态学重构 (Morphological reconstruction, MR)和区域生长法 (Region growing, RG)对变换后的图像进行分割; 最后, 对以上两种方法分割的视网膜血管和背景像素点重新标记识别, 得到视网膜血管最终分割结果. 通过对DRIVE和STARE数据库视网膜图像的分割实验, 证明了该算法的有效性.     

基于PST和多尺度高斯滤波的视网膜血管的分割

视网膜血管分割是眼科计算机辅助诊断和大规模眼科疾病筛查系统的基础。为辅助眼科医生进行眼底疾病的诊断，文中提出了一种基于相位拉伸变换(PST)和多尺度高斯滤波的视网膜血管分割方法。首先，将彩色眼底影像的绿色通道分量图进行增强预处理；然后采用不同尺度的高斯滤波器对预处理增强后的视网膜血管进行降噪处理，再结合PST边缘检测算法初步获得视网膜血管分割图；最后整合初步获得的视网膜血管分割图并进行形态学去噪，获得最终的视网膜血管分割图。通过在视网膜图像库DRIVE上进行实验，其平均准确率为93%，平均灵敏度达77%，平均特异性为95%，该实验结果验证了文中方法的有效性。     

Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy

Diabetic retinopathy screening involves assessment of the retina with attention to a series of indicative features, i.e., blood vessels, optic disk and macula etc. The detection of changes in blood vessel structure and flow due to either vessel narrowing, complete occlusions or neovascularization is of great importance. Blood vessel segmentation is the basic foundation while developing retinal screening systems since vessels serve as one of the main retinal landmark features. This article presents an automated method for enhancement and segmentation of blood vessels in retinal images. We present a method that uses 2-D Gabor wavelet for vessel enhancement due to their ability to enhance directional structures and a new multilayered thresholding technique for accurate vessel segmentation. The strength of proposed segmentation technique is that it performs well for large variations in illumination and even for capturing the thinnest vessels. The system is tested on publicly available retinal images databases of manually labeled images, i.e., DRIVE and STARE. The proposed method for blood vessel segmentation achieves an average accuracy of 94.85% and an average area under the receiver operating characteristic curve of 0.9669. We compare our method with recently published methods and experimental results show that proposed method gives better results.     

空洞残差U型网络用于视网膜血管分割

青光眼是一种不可逆转的致盲性眼科疾病,应当早发现和早治疗。但人工诊断是费时费力的过程,而且受基层医疗资源的限制,人工诊断很容易产生漏诊和误诊的现象。因此,利用深度学习技术辅助诊断眼疾病具有重大意义。如何更为准确且有效地分割视网膜血管成为眼疾病辅助诊断的研究热点问题。于是,基于U型网络（U-Net）提出一种新的网络结构称为空洞残差U型网络（Atrous Residual U-Net,AR-Unet）。在AR-Unet中,为了避免U-Net中的梯度消失以及图像结构信息丢失等问题,将残差网络（ResNet）引入到U-Net中。为了扩大感受野和提高物体间的相关性,再将空洞卷积（Atrous Convolution）整合到U-Net中,从而使得血管分割更加精确。在三个公开的彩色眼底图像数据集DRIVE、STARE和CHASE上进行大量实验,结果表明在不同评价指标下,AR-Unet方法的性能均要优于大多数对比方法。     

Supervised vessel delineation in retinal fundus images with the automatic selection of <Emphasis Type="Italic">B</Emphasis>-COSFIRE filters

The inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background. We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method, we employ a set of B-COSFIRE filters selective for vessels and vessel-endings. Such a set is determined in an automatic selection process and can adapt to different applications. We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE, demonstrate the effectiveness of the proposed approach.     

基于条件深度卷积生成对抗网络的视网膜血管分割

视网膜血管的分割帮助医生对眼底疾病进行诊断有着重要的意义.但现有方法对视网膜血管的分割存在着各种问题, 例如对血管分割不足, 抗噪声干扰能力弱, 对病灶敏感等.针对现有血管分割方法的缺陷, 本文提出使用条件深度卷积生成对抗网络的方法对视网膜血管进行分割.我们主要对生成器的网络结构进行了改进,在卷积层引入残差模块进行差值学习使得网络结构对输出的改变变得敏感, 从而更好地对生成器的权重进行调整.为了降低参数数目和计算, 在使用大卷积核之前使用小卷积核对输入特征图的通道数进行减半处理.通过使用U型网络的思想将卷积层的输出与反卷积层的输出进行连接从而避免低级信息共享.通过在DRIVE和STARE数据集上对本文的方法进行了验证, 其分割准确率分别为96.08 %、97.71 %, 灵敏性分别达到了82.74 %、85.34 %, $F$度量分别达到了82.08 %和85.02 %, 灵敏度比R2U-Net的灵敏度分别高了4.82 %, 2.4 %.     

High speed detection of retinal blood vessels in fundus image using phase congruency

Detection of blood vessels in retinal fundus image is the preliminary step to diagnose several retinal diseases. There exist several methods to automatically detect blood vessels from retinal image with the aid of different computational methods. However, all these methods require lengthy processing time. The method proposed here acquires binary vessels from a RGB retinal fundus image in almost real time. Initially, the phase congruency of a retinal image is generated, which is a soft-classification of blood vessels. Phase congruency is a dimensionless quantity that is invariant to changes in image brightness or contrast; hence, it provides an absolute measure of the significance of feature points. This experiment acquires phase congruency of an image using Log-Gabor wavelets. To acquire a binary segmentation, thresholds are applied on the phase congruency image. The process of determining the best threshold value is based on area under the relative operating characteristic (ROC) curve. The proposed method is able to detect blood vessels in a retinal fundus image within 10 s on a PC with (accuracy, area under ROC curve) = (0.91, 0.92), and (0.92, 0.94) for the STARE and the DRIVE databases, respectively.     

Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering,fuzzy entropy and skeletonization

The analysis of retina blood vessels in clinics indices is one of the most efficient methods employed for diagnosing diseases such as diabetes, hypertension and arthrosclerosis. In this paper, an efficient algorithm is proposed that introduces a higher ability of segmentation by employing Skeletonization and a threshold selection based on Fuzzy Entropy. In the first step, the blurring noises caused by hand shakings during ophthalmoscopy and color photography imageries are removed by a designed Wiener’s filter. Then, in the second step, a basic extraction of the blood vessels from the retina based on an adaptive filtering is obtained. At the last step of the proposed method, an optimal threshold for discriminating main vessels of the retina from other parts of the tissue is achieved by employing fuzzy entropy. Finally, an assessment procedure based on four different measurement techniques in the terms of retinal fundus colors is established and applied to DRIVE and STARE database images. Due to the evaluation comparative results, the proposed extraction of retina blood vessels enables specialists to determine the progression stage of potential diseases, more accurate and in real-time mode.     

基于不同尺度增强融合的视网膜血管分割算法

针对现有视网膜血管图像提取细小血管准确率较低的问题,提出了一种基于多尺度线性检测器与局部和全局增强相结合的视网膜血管分割方法.对多尺度线检测器进行研究,将其分为小尺度和大尺度两部分;利用小尺度对局部增强后的图像与大尺度对全局增强后的图像分别进行检测,得到不同尺度下的响应函数;将不同尺度下的响应函数进行融合,得到最终的视网膜血管结构.在STARE和DRIVE两个数据库上进行实验,结果表明:该算法得到的平均血管准确率分别达到96.62％和96.45％,平均真阳性率分别达到75.52％和83.07％,分割准确率高,能够得到较好的血管分割结果.     

一种浅层非对称结构的视网膜血管分割网络

针对传统视网膜血管分割网络随着网络深度加深导致微小特征信息丢失,网络分割灵敏度低的问题,提出了一种有别于传统对称编码-解码模块的非对称视网膜血管分割结构。网络权重参数量为7.2MB,以残差注意力模块和多尺度空洞卷积模块作为基础特征提取模块,特征图的最大通道层数只有64层,特征图尺寸减半和反卷积操作都只有两次,能够减少特征图尺寸变化带来的信息丢失现象。本文所提方法在DRIVE和CHASE-DB1数据集上进行测试的准确性分别为96.85%和97.39%,灵敏度分别为84.03%和86.50%,特异性分别为98.08%和98.12%,AUC分别为98.63%和98.99%。     

A novel optic disc detection scheme on retinal images

Robust and effective optic disc detection is a necessary processing component in automatic retinal screening systems. In this paper, optic disc localization is achieved by a novel illumination correction operation, and contour segmentation is completed by a supervised gradient vector flow snake (SGVF snake) model. Conventional GVF snake is not sufficient to segment contour due to vessel occlusion and fuzzy disc boundaries. In view of this reason, the SGVF snake is extended in each time of deformation iteration, so that the contour points can be classified and updated according to their corresponding feature information. The classification relies on the feature vector extraction and the statistical information generated from training images. This approach is evaluated by means of two publicly available databases, Digital Retinal Images for Vessel Extraction (DRIVE) database and Structured Analysis of the Retina (STARE) database, of color retinal images. The experimental results show that the overall performance is with 95% correct optic disc localization from the two databases and 91% disc boundaries are correctly segmented by the SGVF snake algorithm.