Automated techniques for blood vessels segmentation through fundus retinal images: A review

Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.     

基于Hessian矩阵的视网膜血管中心线提取

为实现视网膜血管在临床诊断中的重要作用,提出了一种基于Hessian矩阵的视网膜血管提取方法,该方法通过图像预处理增强血管信息,利用血管的微分几何特性,采用离散高斯核对眼底图像进行卷积,结合Hessian矩阵计算血管方向,通过连接算法得到视网膜血管的分布情况.实验结果表明,该方法提取血管中心线的精度可达亚像素级,对不同眼底照相机拍摄的眼底图像可根据血管宽度进行多尺度快速分割.     

基于U型卷积网络的视网膜血管分割方法

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。为此,提出了一种U型卷积网络,实现了更高效的自动化视网膜血管分割。骨干网络基于经典的编解码架构,编码器采用预训练的残差模块充分提取每一层的特征,解码器通过转置卷积逐层进行上采样,增加了特征的复用性。网络在中间层引入ASPP(Atrous Spatial Pyramid Pooling)模块,提取不同尺度的视网膜血管特征。为了在类内预测上保持一致,在跳级层利用通道注意力模块对特征进行自适应细化,融合了不同层次的特征。在DRIVE数据集上的实验结果表明,与其他相关算法性能相比,该算法的敏感性、特异性、准确率均最高,模型泛化能力好,大大提高了视网膜血管分割的准确性。     

自适应脉冲耦合神经网络与匹配滤波器相结合的视网膜血管分割

针对眼底图像中血管与背景间对比度低以及血管自身结构复杂等因素对视网膜血管分割所带来的问题,本文提出了一种具有自适应连接值的脉冲耦合神经网络(PCNN)与高斯匹配滤波器相结合的视网膜血管分割方法。首先,利用对比度受限制的自适应直方图均衡化(CLAHE)技术与二维高斯匹配滤波器对血管区域的对比度进行有效增强。然后,利用经验阈值选择出一定的血管区域作为初始种子区域。接着,将带有快速连接机制的PCNN与种子区域增长思想相结合,通过自适应动态设置PCNN中的连接强度系数和停止条件,实现眼底图像中血管区域的自动生长。整个算法在DRIVE视网膜图像库中进行了测试,实验结果表明,相比于不使用动态连接强度系数与停止条件的方法,所提出算法的灵敏度从49.79%提高至70.39%,准确度从95%提高到95.39%。证明了该算法具有较好的分割精确度和应用价值。     

彩色视网膜眼底图像血管自动检测方法

为了给视网膜图像配准、光照校正及视网膜内部病理学检测等问题提供有效依据,本文提出一种有效检测及识别彩色视网膜眼底图像血管的全自动方法。针对视网膜可见血管呈长条型管状、局部具有较好直线型结构的形态特点,本文采取适用于条状结构的组合移位滤波响应模型进行特征提取。针对血管和血管末端特征的不同,分别配置对称和非对称的两种滤波模型进行跟踪,利用组合移位滤波模型(对称和非对称)获取到的响应及G通道像素灰度值共同构建特征向量库,采用AdaBoost分类器对各个像素点进行分类判定。基于国际公共数据库DRIVE与STARE的实验结果表明,该方法针对两个标准数据库的分割结果(DRIVE:Accuracy=0.948 9,Sensitivity=0.765 7,Specificity=0.980 9;STARE:Accuracy=0.956 7,Sensitivity=0.771 7,Specificity=0.976 6)均优于已有方法,适用于彩色视网膜眼底图像的计算机辅助定量分析,可作为临床借鉴。     

融合下补偿结构的眼底血管图像分割网络研究

眼底血管图像在临床中通常被用于眼部疾病的诊断及监测，其中血管的形态结构能够反映疾病的重要特征，因此，眼底血管图像的分割处理对眼部疾病的诊断和预防具有十分重要的医学意义。针对目前人工智能主流算法中卷积和池化操作会导致很多特征丢失，提取特征时会忽视图像中的空间信息，图像中的细小血管很难分割出来等问题，基于U-net模型进行了相关研究，结合空间注意力模块对空间特征进行细化，同时提出了一种下补偿结构LCSAnet。该结构能够减少网络提取特征信息过程中的特征损失，从而提高分割精度。研究实验在DRIVE数据集上完成，LC-SAnet的分割准确率达到96.97%,F1值达到74.36%。结果证明，LC-SAnet表现出更好的分割性能，对细小血管的结构识别更加准确。     

视网膜图像中的血管自适应提取

观察和分析视网膜血管网络是分析和诊断心血管疾病的重要方法.根据视网膜血管图像的灰度分布特征,提出了一种自适应阈值分割的血管提取方法.此方法首先将视网膜图像分为若干同样大小的区域;然后计算出每一区域满足梯度要求的像素点个数;接着对所有满足梯度个数的区域计算其局部阈值,并用该局部阈值进行二值化.经过以上处理后,能得到有部分断开的血管和一些分散的碎片,再经过后处理连接断开的血管,并清除孤立的碎片,得到视网膜血管网络.实验证明:该算法处理速度较快,并能提取到大部分血管主干.     

Automated system for characterization and classification of malaria‐infected stages using light microscopic images of thin blood smears

In this paper, we propose a comprehensive image characterization cum classification framework for malaria‐infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three‐image segmentation algorithms (namely, rule‐based, Chan–Vese‐based and marker‐controlled watershed methods), marker‐controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F‐statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features’ subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria‐infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F‐statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria‐infected stage classification.     

心血管造影图像的分割与诊断

针对心血管造影图像,提出了一种新的血管分割方法,并实现了血管狭窄位置的自动诊断。血管分割中提出了基于Hessian矩阵多尺度增强的新血管函数,在增强的特征图上采用了种子点的自动化选取和双阶段区域增长的分割方法提取了血管轮廓。在此基础上进行了血管骨架提取,骨架点搜索和直径测量,实现了全自动的狭窄诊断。实验结果表明,新的血管分割方法能够提取出较精准的主分支轮廓,对细小血管也有良好的增强和分割效果,血管狭窄的自动诊断,计算速度快,诊断结果较准确,能够对医生的最终判断提供辅助参考和量化依据。     

Segmentation of digitized histological sections for quantification of the muscularized vasculature in the mouse hind limb

Immunohistochemical tissue staining enhances microvasculature characteristics, including the smooth muscle in the medial layer of the vessel walls that is responsible for regulation of blood flow. The vasculature can be imaged in a comprehensive fashion using whole‐slide scanning. However, since each such image potentially contains hundreds of small vessels, manual vessel delineation and quantification is not practically feasible. In this work, we present a fully automatic segmentation and vasculature quantification algorithm for whole‐slide images. We evaluated its performance on tissue samples drawn from the hind limbs of wild‐type mice, stained for smooth muscle using 3,3'‐Diaminobenzidine (DAB) immunostain. The algorithm was designed to be robust to vessel fragmentation due to staining irregularity, and artefactual staining of nonvessel objects. Colour deconvolution was used to isolate the DAB stain for detection of vessel wall fragments. Complete vessels were reconstructed from the fragments by joining endpoints of topological skeletons. Automatic measures of vessel density, perimeter, wall area and local wall thickness were taken. The segmentation algorithm was validated against manual measures, resulting in a Dice similarity coefficient of 89%. The relationships observed between these measures were as expected from a biological standpoint, providing further reinforcement of the accuracy of this system. This system provides a fully automated and accurate means of measuring the arteriolar and venular morphology of vascular smooth muscle.     

Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection

Among precision medical techniques, medical image processing is rapidly growing as a successful tool for cancer detection. Skin cancer is one of the crucial cancer types. It is identified through computer vision (CV) techniques using dermoscopic images. The early diagnosis skin cancer from dermoscopic images can be decrease the mortality rate. We propose an automated system for skin lesion detection and classification based on statistical normal distribution and optimal feature selection. Local contrast is controlled using a brighter channel enhancement technique, and segmentation is performed through a statistical normal distribution approach. The multiplication law of probability is implemented for the fusion of segmented images. In the feature extraction phase, optimized histogram, optimized color, and gray level co‐occurrences matrices features are extracted and covariance‐based fusion is performed. Subsequently, optimal features are selected through a binary grasshopper optimization algorithm. The selected optimal features are finally fed to a classifier and evaluated on the ISBI 2016 and ISBI 2017 data sets. Classification accuracy is computed using different Support Vector Machine (SVM) kernel functions, and the best accuracy is obtained for the cubic function. The average accuracies of the proposed segmentation on the PH2 and ISBI 2016 data sets are 93.79 and 96.04%, respectively, for an image size 512 × 512. The accuracies of the proposed classification on the ISBI 2016 and ISBI 2017 data sets are 93.80 and 93.70%, respectively. The proposed system outperforms existing methods on selected data sets.     

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.     

一种改进的高斯混合模型的玻璃缺陷分割方法

针对传统玻璃缺陷检测技术准确率较低、时间长、精度低等难点,提出了一种改进高斯混合模型的玻璃缺陷图像分割方法。首先,基于分数阶微分运算获取灰度特征,并利用灰度共生矩阵提取纹理特征,构建玻璃缺陷完整的双特征观测数据;然后,引入相邻像素间的空间关联性和约束性,通过交替进行基于双特征随机场评估像素点与标号场之间的对应关系和空间约束来完成玻璃缺陷分割;最后,在不同温度系数参数β下对分割算法进行了性能测试实验,同时,与当前流行的分割算法对4种不同类型的玻璃缺陷进行了性能比较实验。实验表明该算法能够提高图像分割的鲁棒性和精确性。     

Fast linear feature detection using multiple directional non-maximum suppression

The capacity to detect linear features is central to image analysis, computer vision and pattern recognition and has practical applications in areas such as neurite outgrowth detection, retinal vessel extraction, skin hair removal, plant root analysis and road detection. Linear feature detection often represents the starting point for image segmentation and image interpretation. In this paper, we present a new algorithm for linear feature detection using multiple directional non-maximum suppression with symmetry checking and gap linking. Given its low computational complexity, the algorithm is very fast. We show in several examples that it performs very well in terms of both sensitivity and continuity of detected linear features.     

基于主曲率和主方向的多尺度视网膜血管骨架提取方法

为了减少视网膜血管骨架提取过程中低对比度血管的漏检和误检数量,提出了一种基于主曲率和主方向的多尺度视网膜血管骨架提取方法。首先,分别提取视网膜图像中每个像素点在多尺度高斯滤波后的主曲率和主方向;其次,在每个尺度下分别提取最大主曲率方向上的局部最大值点,并通过曲率阈值筛选出高对比度血管中心像素点作为种子点;然后,利用最小主方向对低对比度血管进行骨架追踪和标记;最后,对多个尺度下提取的血管骨架进行融合。方法分别在DRIVE训练数据集、DRIVE测试数据集和STARE数据集上进行了测试,漏检数量分别为89、97、106,误检数量分别为99、101、122。实验结果表明,该方法能够提取出低对比度的细小血管骨架,但对于对比度在3个灰度级以内的细小血管存在少量漏检,对于与血管粘连的高对比度细条状纹理和病灶干扰存在少量误检。     

基于粒子群优化算法的水源微生物自动识别

水源微生物检测在水源生物安全监测等方面具有非常重要的意义，而传统的显微镜观测等方法存在效率低、需要专业人员操作等不足，为此提出了一种水源微生物自动识别方法。采集水样，并制作水源微生物图像集，编写全自动与半自动两种图像分割算法用于提取目标微生物区域，并提取6种图像特征。基于以上特征数据，研究水源微生物识别模型的优化问题：首先，优化部分特征参数；接着，融合所有特征，建立粒子群优化算法的支持向量机（support vector machine optimized by particle swarm optimization, PSO-SVM）微生物识别模型，并与其他识别算法进行比较。结果表明，相比于其他3种算法，PSO-SVM能更有效地识别各种微生物，其平均识别率达到97.08%。     

A multiangle polarised imaging-based method for thin section segmentation

The most crucial task of petroleum geology is to explore oil and gas reservoirs in the deep underground. As one of the analysis techniques in petroleum geological research, rock thin section identification method includes particle segmentation, which is one of the key steps. A conventional sandstone thin section image typically contains hundreds of mineral particles with blurred boundaries and complex microstructures inside the particles. Moreover, the complex lithology and low porosity of tight sandstone make traditional image segmentation methods unsuitable for solving the complex thin section segmentation problems. This paper combines petrology and image processing technologies. First, polarised sequence images are aligned, and then the images are transformed to the HSV colour space to extract pores. Second, particles are extracted according to their extinction characteristics. Last, a concavity and corner detection matching method is used to process the extracted particles, thereby completing the segmentation of sandstone thin section images. The experimental results show that our proposed method can more accurately fit the boundaries of mineral particles in sandstone images than existing image segmentation methods. Additionally, when applied in actual production scenarios, our method exhibits excellent performance, greatly improving thin section identification efficiency and significantly assisting experts in identification.     

基于集成学习的铸件缺陷识别方法

针对铸件图像噪声多和对比度不足引起的缺陷识别困难的问题,文中提出了一种基于集成学习的铸件缺陷识别方法。首先,该方法采用灰度变换法、双边滤波以及自适应图像分割法对铸件图像进行预处理。然后,通过提取方向梯度直方图（Histogram of Oriented Gradients, HOG）特征、不变矩特征和局部二值模式（Local Binary Pattern, LBP）纹理特征构建全信息特征集,并结合支持向量机递归特征消除（Support Vector Machine-Recursive Feature Elimination, SVM-RFE）算法筛选铸件缺陷敏感特征。最后,利用Adaboost-RF（Adaptive Boosting-Random Forest）方法构建铸件缺陷识别模型。对比实验结果表明,该模型不仅可以有效提取缺陷敏感特征,而且相较于其他分类器具有更好的分类性能和泛化能力。     

Automatic macroscopic density artefact removal in a Nissl‐stained microscopic atlas of whole mouse brain

Acquiring a whole mouse brain at the micrometer scale is a complex, continuous and time‐consuming process. Because of defects caused by sample preparation and microscopy, the acquired image data sets suffer from various macroscopic density artefacts that worsen the image quality. We have to develop the available preprocessing methods to improve image quality by removing the artefacts that effect cell segmentation, vascular tracing and visualization. In this study, a set of automatic artefact removal methods is proposed for images obtained by tissue staining and optical microscopy. These methods significantly improve the complicated images that contain various structures, including cells and blood vessels. The whole mouse brain data set with Nissl staining was tested, and the intensity of the processed images was uniformly distributed throughout different brain areas. Furthermore, the processed image data set with its uniform brightness and high quality is now a fundamental atlas for image analysis, including cell segmentation, vascular tracing and visualization.     

基于多尺度颜色替换的角膜老年环分割

角膜老年环是一种在角膜边缘形成的白色环状改变,主要由于人体脂类代谢异常而产生。通过图像分析的方法对角膜老年环进行检测可以方便、及时帮助人们发现身体脂类代谢异常状况。自然睁开状态下获取的彩色图像,角膜老年环经常被眼睑随机遮挡,而且被光斑、血管等因素的干扰。为了提高算法鲁棒性,减少由于眼睑的随机遮挡造成的定位失误,提出了一种基于多尺度颜色替换的角膜老年环分割方法。首先对图像进行量化;其次,在不同尺度模板下对图像按照本文定义的颜色替换策略进行处理,并最终实现目标分割。实验结果表明,在采集的1 968幅图像中该方法能够达到97.0%的分割正确率,所用算法具有较高的鲁棒性。