基于视网膜血管模型的图像分割与血管提取

本文采用非线性图像处理方法,对眼底图像进行去噪增强和分析,提取血管中心线,以便于对视网膜血管网络进行定量描述和定量分析。实验表明该方法能好地从非荧光眼底图像中提取出膜血管网络。     

基于可控图像分割的快速视网膜血管提取算法

针对多数视网膜血管提取算法实时性不强和分割 精度不高的问题,提出了一种基于可控图像分割的 快速视网膜血管提取算法。首先,对视网膜G分量图像的灰 度进行反转和自适应直方图均衡化,应用结 构元素为“菱形”和“圆盘形”的形态学“开”运算平滑图像背景和增强血管对比度,消除 视盘后阈值分割并二值 化得到不含视盘的分割图像。其次,根据在灰度图像中检测到的视盘构建掩膜,再次对 视网膜绿色分 量图像自适应直方图均衡化后进行阈值分割,并和掩膜进行逻辑“与”运算得到含有掩膜的 分割图像。最后, 将不含视盘的分割图像与含有掩膜的分割图像进行逻辑“与”运算,并融合边界信息获得最 终的视网膜血管 结构。实验结果表明,本文算法能有效提取视网膜眼底图像的血管网络,有较强的实时性和 较高的分割精度。     

一种新的视网膜血管网络自动分割方法

提出了一种基于脉冲耦合神经网络(PCNN)和分布式遗传算法(DGA)的视网膜血管自动分割方法.首先采用二维高斯匹配滤波器预处理以增强血管,然后采用DGA快速搜索出PCNN的最佳参数设置值并运用PCNN分割出增强图像的血管网络,最后对分割得到的血管网络结合区域连通性特征,采用面积滤波算子滤除噪声,提取出最终的血管网络.通过在国际上公开的Hoover眼底图像库中的实验,结果表明,该方法在血管分支提取和算法有效性方面明显优于Hoover算法,具有较高的临床应用价值.     

基于多路径输入和多尺度特征融合的视网膜血管分割

视网膜血管的形态变化,如分叉角度、扩张程度等 ,可为眼底疾病的诊断提供依据。 使用深度学习技术对视网膜病变程度进行评估成为目前研究的重点。提出了一种基于多 路径输入和多尺度特征融合的视网膜血管分割方法来解决视网膜血管分割问题。采用了 多路径输入和多特征融合的方式改进了U-Net模型,使本文的网络能够有效的解决眼底视网 膜图像的分割效果差的 问题。实验结果表明,算法在DRIVE和CHASE_DB1数据集上,敏 感性分别取得0.814和0.813,特异性 分别取得0.984和0.986,在分割准确率指标上 分别取得0.969和0.975,所提方法相较于其他方法较优。     

一种基于改进的PCNN的视网膜血管树提取方法

根据脉冲耦合神经网络（PCNN）动态点火特性和视网膜血管网络区域结构特征,提出了一种基于改进型PCNN（IPCNN）的视网膜血管树提取方法。该方法对二维高斯匹配滤波预处理增强后的眼底图像运用IPCNN分割出增强图像的血管网络,然后对分割得到的血管网络结合区域连通性特征,采用长度滤波算子滤除噪声,提取出最终的血管树。通过...     

基于概率跟踪的冠状动脉造影图像的血管树提取

采用传统的基于跟踪的方法分割冠状动脉X射线造影图像的血管时,易受血管节点和曲率的影响.对此,本文提出了基于多特征测度的概率跟踪模型和中心线优化算子,该方法能够准确地分割出血管树的骨架并进行中心线优化.实验中,该方法不仅避免了传统方法的弊端,而且具有较低的计算耗时、较高的鲁棒性、全自动性:一次选定跟踪的起点,可以自动提取出80%以上冠状动脉树.     

基于多模型融合和区域迭代生长的视网膜血管自动分割

糖尿病视网膜病变是成年人致盲首因,视网膜血管分割是诊断糖尿病视网膜病变的基础.为提高视网膜血管分割准确性,提出一种基于多模型融合和区域迭代生长的视网膜血管自动分割算法.首先,预处理后分别构建数学形态学、匹配滤波器、尺度空间分析、多尺度线检测和神经网络模型初步分割视网膜血管,为减少噪声取五个分割结果的均值作为初步输出.其次,设计掩膜分离渗出物和视盘,将数学形态学模型分割结果替换掩膜白色区域,并融合初步输出生成组合结果.最后,考虑视网膜血管先验知识,对组合结果阈值分割和区域迭代生长后获取最终结果.实验结果表明,该算法分割DRIVE和STARE眼底图像库视网膜血管的检测精度、敏感度和特异性分别为0.9457、0.7843、0.9815以及0.9472、0.7826、0.9803,优于多数经典算法.     

基于过渡区提取的视网膜血管分割方法

 针对现有视网膜血管分割方法对于小血管和低对比度血管分割效果差的问题,提出了一种基于过渡区提取的视网膜血管分割方法.该方法首先采用二维高斯匹配滤波预处理以增强血管,然后采用基于最佳熵的方法提取主血管、采用基于分布式遗传算法和Otsu相结合的方法提取过渡区,最后利用区域连通性分析所提取的主血管和过渡区,分割出最终的血管.通过在Hoover眼底图像库中的实验,结果表明该方法在小血管的提取、连通性和有效性方面均优于Hoover算法,另外由于迁移策略的分布式遗传算法的引入,使得算法效率也明显提高.     

MS-UNet++:基于改进UNet++的视网膜血管分割

本文针对视网膜图像中细微血管特征提取困难导致其分割难度高等问题,提出了一种 基于端到端的神经网络嵌套视网膜血管分割模型算法(简称MS-UNet++),该算法选取了深度监督网络UNet++作为分割网络模型,提升特征的使用效率;引入MulitRes模块,改善低对比度环境下细小血管的特征学习效果,并在特征提取后加上SENet模块进行挤压和激励操作,从而增强特征提取阶段的感受野,提高目标相关特征通道的权重。基于DRIVE图像数据集的实验结果表明,该算法分割结果与真实结果之间的重叠率DICE值为83.64%,并交比IOU为94.83%,准确度ACC为96.79%,灵敏度SE为81.78%,较现有模型有一定的提升,可用于视网膜图像血管分割,为临床诊断提供辅助信息。     

基于RAU-net的视网膜血管图像分割

在眼科疾病的诊断中,对视网膜血管进行分割是非常有效的一种方法。在方法使用中,经常会遇到由于视网膜血管背景对比度低及血管末梢细节复杂导致的血管分割难度较大的问题,通过在设计网络的过程中在基础U-net网络中引入残差学习,注意力机制等模块,并将两者巧妙地结合在一起,提出一种新型的基于U-net的RAU-net视网膜血管图像分割算法。首先,在网络的编码器阶段加入残差模块,解决了模型网络加深导致梯度爆炸以及梯度消失的问题。其次,在网络的解码器阶段引入注意力门（attention gate, AU）模块,用来抑制不必要的特征,从而使模型产生更高的精度。通过在DRIVE数据集上进行验证,该算法的准确率、灵敏度、特异性和F₁-score分别达到了0.7832,0.9815,0.9568和0.8192。分割效果相对于普通监督学习算法较为良好。     

Improved detection of the central reflex in retinal vessels using a generalized dual-gaussian model and robust hypothesis testing

This updates an earlier publication by the authors describing a robust framework for detecting vasculature in noisy retinal fundus images. We improved the handling of the "central reflex" phenomenon in which a vessel has a "hollow" appearance. This is particularly pronounced in dual-wavelength images acquired at 570 and 600 nm for retinal oximetry. It is prominent in the 600 nm images that are sensitive to the blood oxygen content. Improved segmentation of these vessels is needed to improve oximetry. We show that the use of a generalized dual-Gaussian model for the vessel intensity profile instead of the Gaussian yields a significant improvement. Our method can account for variations in the strength of the central reflex, the relative contrast, width, orientation, scale, and imaging noise. It also enables the classification of regular and central reflex vessels. The proposed method yielded a sensitivity of 72% compared to 38% by the algorithm of Can et al., and 60% by the robust detection based on a single-Gaussian model. The specificity for the methods were 95%, 97%, and 98%, respectively.     

Ridge-based vessel segmentation in color images of the retina

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kappaNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. The results show that our method is significantly better than the two rule-based methods (p < 0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.     

Detection of optic disc in retinal images by means of a geometrical model of vessel structure

We present here a new method to identify the position of the optic disc (OD) in retinal fundus images. The method is based on the preliminary detection of the main retinal vessels. All retinal vessels originate from the OD and their path follows a similar directional pattern (parabolic course) in all images. To describe the general direction of retinal vessels at any given position in the image, a geometrical parametric model was proposed, where two of the model parameters are the coordinates of the OD center. Using as experimental data samples of vessel centerline points and corresponding vessel directions, provided by any vessel identification procedure, model parameters were identified by means of a simulated annealing optimization technique. These estimated values provide the coordinates of the center of OD. A Matlab prototype implementing this method was developed. An evaluation of the proposed procedure was performed using the set of 81 images from the STARE project, containing images from both normal and pathological subjects. The OD position was correctly identified in 79 out of 81 images (98%), even in rather difficult pathological situations.     

A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields

In this paper, a method is proposed for detecting blood vessels in pathological retina images. In the proposed method, blood vessel-like objects are extracted using the Laplacian operator and noisy objects are pruned according to the centerlines, which are detected using the normalized gradient vector field. The method has been tested with all the pathological retina images in the publicly available STARE database. Experiment results show that the method can avoid detecting false vessels in pathological regions and can produce reliable results for healthy regions.     

Enhancement of blood vessels in retinal imaging using the nonsubsampled contourlet transform

This paper presents an enhancement method for blood vessels in retinal images based on the nonsubsampled contourlet transform (NSCT). The NSCT is a shift-invariant version of the contourlet transform built upon the nonsubsampled pyramid filter banks and the nonsubsampled directional filter banks. The proposed method uses the NSCT to decompose the input retinal image into eight directions from coarser to finer scales, and then analyzes and classifies the image pixels into three categories: vessel, uncertainty, and non-vessel pixels, according to the NSCT coefficients. Then, we modify the NSCT coefficients according to the class of each pixel using a nonlinear mapping function, and reconstruct the enhanced image from the modified NSCT coefficients. The experimental results show that the proposed method can obviously increase the contrast of retinal vessels and thus outperform other enhancement methods.     

Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter

Optic disc (OD) detection is a main step while developing automated screening systems for diabetic retinopathy. We present in this paper a method to automatically detect the position of the OD in digital retinal fundus images. The method starts by normalizing luminosity and contrast through out the image using illumination equalization and adaptive histogram equalization methods respectively. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Hence, a simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity. The retinal vessels are segmented using a simple and standard 2-D Gaussian matched filter. Consequently, a vessels direction map of the segmented retinal vessels is obtained using the same segmentation algorithm. The segmented vessels are then thinned, and filtered using local intensity, to represent finally the OD-center candidates. The difference between the proposed matched filter resized into four different sizes, and the vessels' directions at the surrounding area of each of the OD-center candidates is measured. The minimum difference provides an estimate of the OD-center coordinates. The proposed method was evaluated using a subset of the STARE project's dataset, containing 81 fundus images of both normal and diseased retinas, and initially used by literature OD detection methods. The OD-center was detected correctly in 80 out of the 81 images (98.77%). In addition, the OD-center was detected correctly in all of the 40 images (100%) using the publicly available DRIVE dataset.     

Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction

This paper presents an automated method for the segmentation of the vascular network in retinal images. The algorithm starts with the extraction of vessel centerlines, which are used as guidelines for the subsequent vessel filling phase. For this purpose, the outputs of four directional differential operators are processed in order to select connected sets of candidate points to be further classified as centerline pixels using vessel derived features. The final segmentation is obtained using an iterative region growing method that integrates the contents of several binary images resulting from vessel width dependent morphological filters. Our approach was tested on two publicly available databases and its results are compared with recently published methods. The results demonstrate that our algorithm outperforms other solutions and approximates the average accuracy of a human observer without a significant degradation of sensitivity and specificity.     

An Active Contour Model for Segmenting and Measuring Retinal Vessels

This paper presents an algorithm for segmenting and measuring retinal vessels, by growing a “Ribbon of Twins” active contour model, which uses two pairs of contours to capture each vessel edge, while maintaining width consistency. The algorithm is initialized using a generalized morphological order filter to identify approximate vessels centerlines. Once the vessel segments are identified the network topology is determined using an implicit neural cost function to resolve junction configurations. The algorithm is robust, and can accurately locate vessel edges under difficult conditions, including noisy blurred edges, closely parallel vessels, light reflex phenomena, and very fine vessels. It yields precise vessel width measurements, with subpixel average width errors. We compare the algorithm with several benchmarks from the literature, demonstrating higher segmentation sensitivity and more accurate width measurement.      

Robust 3-D modeling of vasculature imagery using superellipsoids

This paper presents methods to model complex vasculature in three-dimensional (3-D) images using cylindroidal superellipsoids, along with robust estimation and detection algorithms for automated image analysis. This model offers an explicit, low-order parameterization, enabling joint estimation of boundary, centerlines, and local pose. It provides a geometric framework for directed vessel traversal, and extraction of topological information like branch point locations and connectivity. M-estimators provide robust region-based statistics that are used to drive the superellipsoid toward a vessel boundary. A robust likelihood ratio test is used to differentiate between noise, artifacts, and other complex unmodeled structures, thereby verifying the model estimate. The proposed methodology behaves well across scale-space, shows a high degree of insensitivity to adjacent structures and implicitly handles branching. When evaluated on synthetic imagery mimicking specific structural complexities in tumor microvasculature, it consistently produces ubvoxel accuracy estimates of centerlines and widths in the presence of closely-adjacent vessels, branch points, and noise. An edit-based validation demonstrated a precision level of 96.6% at a recall level of 95.4%. Overall, it is robust enough for large-scale application.     

Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features

This paper presents an automated method to identify arteries and veins in dual-wavelength retinal fundus images recorded at 570 and 600 nm. Dual-wavelength imaging provides both structural and functional features that can be exploited for identification. The processing begins with automated tracing of the vessels from the 570-nm image. The 600-nm image is registered to this image, and structural and functional features are computed for each vessel segment. We use the relative strength of the vessel central reflex as the structural feature. The central reflex phenomenon, caused by light reflection from vessel surfaces that are parallel to the incident light, is especially pronounced at longer wavelengths for arteries compared to veins. We use a dual-Gaussian to model the cross-sectional intensity profile of vessels. The model parameters are estimated using a robust -estimator, and the relative strength of the central reflex is computed from these parameters. The functional feature exploits the fact that arterial blood is more oxygenated relative to that in veins. This motivates use of the ratio of the vessel optical densities (ODs) from images at oxygen-sensitive and oxygen-insensitive wavelengths () as a functional indicator. Finally, the structural and functional features are combined in a classifier to identify the type of the vessel. We experimented with four different classifiers and the best result was given by a support vector machine (SVM) classifier. With the SVM classifier, the proposed algorithm achieved true positive rates of 97% for the arteries and 90% for the veins, when applied to a set of 251 vessel segments obtained from 25 dual wavelength images. The ability to identify the vessel type is useful in applications such as automated retinal vessel oximetry and automated analysis of vascular changes without manual intervention.