Retinal vessel segmentation using enhanced fuzzy min-max neural network

Automated segmentation of retinal vessels plays a pivotal role in early diagnosis of ophthalmic disorders. In this paper, a blood vessel segmentation algorithm using an enhanced fuzzy min-max neural network supervised classifier is proposed. The input to the network is an optimal 11-D feature vector which consists of spatial as well as frequency domain features extracted from each pixel of a fundus image. The essence of the method is its hyperbox classifier which performs online learning and gives binary output without any need of post-processing. The method is tested on publicly available databases DRIVE and STARE. The results are compared with the existing methods in the literature. The proposed method exhibits efficient performance and can be implemented in computer aided screening and diagnosis of retinal diseases. The method attains an average accuracy, sensitivity and specificity of 95.73%, 74.75% and 97.81% on DRIVE database and 95.51%, 74.65% and 97.11% on STARE database, respectively.

     

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。     

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。     

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.     

基于编解码结构的多特征融合眼底图像分割

为解决现有眼底图像分割方法对于细微血管存在低分割精度和低准确率的问题,提出一种基于编解码结构的U-Net改进网络模型。首先对数据进行预处理与扩充,提取绿色通道图像,并将其通过对比度限制直方图均衡化和伽马变换以增强对比度;其次训练集被输入到用于分割的神经网络中,在编码过程加入残差模块,用短跳跃连接将高、低特征信息融合,并利用空洞卷积增加感受野,解码模块加入注意力机制增加对细微血管分割精度;最后利用训练完成的分割模型进行预测得出视网膜血管分割结果。在DRIVE和CHASE-DB1眼底图像数据集上进行对比实验,模型算法的平均准确率、特异性和灵敏度分别达到96.77%和97.22%、98.74%和98.40%、80.93%和81.12%。实验结果表明该算法能够改善微细血管分割准确率及效率不高的问题,对视网膜血管可以进行更准确的分割。     

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。     

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.     

An approach to localize the retinal blood vessels using bit planes and centerline detection

The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.     

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.     

U-net与Dense-net相结合的视网膜血管提取

目的 视网膜血管健康状况的自动分析对糖尿病、心脑血管疾病以及多种眼科疾病的快速无创诊断具有重要参考价值。视网膜图像中血管网络结构复杂且图像背景亮度不均使得血管区域的准确自动提取具有较大难度。本文通过使用具有对称全卷积结构的U-net深度神经网络实现视网膜血管的高精度分割。方法 基于U-net网络中的层次化对称结构和Dense-net网络中的稠密连接方式,提出一种改进的适用于视网膜血管精准提取的深度神经网络模型。首先使用白化预处理技术弱化原始彩色眼底图像中的亮度不均,增强图像中血管区域的对比度;接着对数据集进行随机旋转、Gamma变换操作实现数据增广;然后将每一幅图像随机分割成若干较小的图块,用于减小模型参数规模,降低训练难度。结果 使用多种性能指标对训练后的模型进行综合评定,模型在DRIVE数据集上的灵敏度、特异性、准确率和AUC（area under the curve）分别达到0.740 9、0.992 9、0.970 7和0.917 1。所提算法与目前主流方法进行了全面比较,结果显示本文算法各项性能指标均表现良好。结论 本文针对视网膜图像中血管区域高精度自动提取难度大的问题,提出了一种具有稠密连接方式的对称全卷积神经网络改进模型。结果表明该模型在视网膜血管分割中能够达到良好效果,具有较好的研究及应用价值。     

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.     

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.     

基于改进的U-Net眼底视网膜血管分割

针对视网膜血管图像特征信息复杂程度高,现有算法存在微血管分割较低和病理信息误分割等问题,提出一种融合DenseNet和U-Net网络的血管分割模型。首先,通过限制对比度直方图均衡化和filter滤波对图像进行血管增强处理;其次利用局部自适应Gamma提升图像亮度信息并降低伪影的干扰;再次,由多尺度形态学滤波局部增强微血管特征信息;最后,利用U型密集连接模块进行分割。该算法在DRIVE数据集上实验,其平均准确率、灵敏度和特异性分别高达96.74%、81.50%和98.20%。     

Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features

Diabetic retinopathy (DR) is the major ophthalmic pathological cause for loss of eye sight due to changes in blood vessel structure. The retinal blood vessel morphology helps to identify the successive stages of a number of sight threatening diseases and thereby paves a way to classify its severity. This paper presents an automated retinal vessel segmentation technique using neural network, which can be used in computer analysis of retinal images, e.g., in automated screening for diabetic retinopathy. Furthermore, the algorithm proposed in this paper can be used for the analysis of vascular structures of the human retina. Changes in retinal vasculature are one of the main symptoms of diseases like hypertension and diabetes mellitus. Since the size of typical retinal vessel is only a few pixels wide, it is critical to obtain precise measurements of vascular width using automated retinal image analysis. This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels are identified by means of a multilayer perceptron neural network, for which the inputs are derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network is utilized in our method. The performance of our technique is evaluated and tested on publicly available DRIVE database and we have obtained illustrative vessel segmentation results for those images.     

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.     

改进的U-Net在视网膜血管分割上的应用

针对眼底视网膜血管分割中血管边界难以精确识别以及血管与背景对比度低而难以分割的问题,提出一种编码器-解码器结构的算法.为了提高算法在血管边界的分割能力,在编码部分采用全局卷积网络(GCN)和边界细化(BR)替换传统的卷积层;在跳跃连接部分引入改进的位置注意模块(PA)和通道注意模块(CA),目的是增加血管与背景之间的对...     

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.