一种视网膜血管自适应提取方法

为了快速有效地提取视网膜血管，根据视网膜图像的灰度分布特征，提出了一种新的基于自适应阈值化的血管提取方法。该方法是首先把图像划分成很多同样尺寸的小子图像，然后在每个子图像中分别计算局部阈值，并用该阈值分割该子图像。因为视网膜图像中血管和背景在局部范围内都比较均匀，所以在每个子图像中都存在一个局部阈值能够将其中的血管分割出来。采用的局部阈值计算方法不仅允许子图像可以取得很小，而且能够保证得到平方误差最小意义下的最优阈值。在阈值计算过程中，还用到一种基于过零点边缘检测技术的边缘追踪算法。最后还提出一种基于区域生长的特征综合方法，即通过综合两次阈值化分割得到的血管结构来清除碎片。多幅视网膜图像的实验证明，该方法的计算速度很快，并且可以提取包括细血管在内的绝大部分血管。     

Three-dimensional reconstruction of moving arterial beds from digital subtraction angiography

A system for three-dimensional reconstruction of dynamic (moving) vascular bed structures has been developed and is described. Input images are obtained from two-view (bi-plane or ECG correlated) X-ray angiograms. A target structure consisting of vessel branch points (nodes) and lines between the branch points is entered on the first of a sequence of images in one view. The movement of the nodes is indicated on subsequent images and on the images of the second view. The target is linearly warped according to the motion of the node points. Automatic edge detection (with subsequent operator correction) is used to detect centerlines and edges of vessels. Three-dimensional reconstruction is accomplished using a distance minimizing point matching technique. Finally, angle-corrected densitometric methods are used to refine the vessel cross section. Standard shaded surface display techniques are then used to display the moving arterial bed. Flow measurements are obtained by tracking the leading edge of the bolus down the three-dimensional arterial tree.     

视网膜血管图像特征点自动提取和分类

由动静脉血管组成的眼底视网膜血管结构的特征点是预测心血管疾病、图像分析和生物学应用的重要特征。把角点检测引入到视网膜血管分叉点和交叉点提取中,利用边缘检测算子得到二值边缘图像,采用基于累加点到弦的距离（CPDA）的角点检测方法得到候选特征点,再根据视网膜血管图像的拓扑结构设计自适应矩形探测器对候选特征点进行删减和分类。实验结果表明,基于CPDA的角点检测和自适应矩形探测器的方法有效地实现了节点的提取和分类。     

Maximum likelihood estimation of vessel parameters from scale space analysis

We describe a method of detecting features in retinal images using a model-based approach. The image is processed using a bank of filters in a scale space. A parametric model of the target feature is then proposed and the filter responses to the model calculated. A noise model is proposed, and incorporated into a maximum likelihood estimator to estimate model parameters. The estimator uses the generative parametric model to explore smoothly the scale space. This method is applied to the detection of retinal blood vessels, using a Gaussian-profiled valley as a model. A simple thresholding method is proposed as an example of using the rich estimated parameter maps to detect vessels and the results are compared against two existing vessel detectors. Our system is compared against ground truth and the output of existing systems. It is found to be comparable and, in addition, produces direct estimates of vessel calibres and contrasts. It does not use any form of region growing or vessel tracking, but thresholds a function of the estimated vessel parameters to determine vessel regions.     

Segmentation of vessels from mammograms using a deformable model

Vessel extraction is a fundamental step in certain medical imaging applications such as angiograms. Different methods are available to segment vessels in medical images, but they are not fully automated (initial vessel points are required) or they are very sensitive to noise in the image. Unfortunately, the presence of noise, the variability of the background, and the low and varying contrast of vessels in many imaging modalities such as mammograms, makes it quite difficult to obtain reliable fully automatic or even semi-automatic vessel detection procedures. In this paper a fully automatic algorithm for the extraction of vessels in noisy medical images is presented and validated for mammograms. The main issue in this research is the negative influence of noise on segmentation algorithms. A two-stage procedure was designed for noise reduction. First, a global approach phase including edge detection and thresholding is applied. Then, the local approach phase performs vessel segmentation using a deformable model with a new energy term that reduces the noise still remaining in the image from the first stage. Experimental results on mammograms show that this method has an excellent performance level in terms of accuracy, sensitivity, and specificity. The computation time also makes it suitable for real-time applications within a clinical environment.     

Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement

We present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 2D and 3D images. The basic tools are minimal paths solved using the fast marching algorithm. This allows interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric. The designed metric is well oriented along the direction of the vessel, admits higher velocity on the centerline, and provides a good estimate of the vessel radius. Based on the optimally oriented flux this measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. We obtain promising results on noisy synthetic and real 2D and 3D images and we present a clinical validation.     

Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is neovascularisation, the growth of abnormal new vessels. This paper describes an automated method for the detection of new vessels in retinal images. Two vessel segmentation approaches are applied, using the standard line operator and a novel modified line operator. The latter is designed to reduce false responses to non-vessel edges. Both generated binary vessel maps hold vital information which must be processed separately. This is achieved with a dual classification system. Local morphology features are measured from each binary vessel map to produce two separate feature sets. Independent classification is performed for each feature set using a support vector machine (SVM) classifier. The system then combines these individual classification outcomes to produce a final decision. Sensitivity and specificity results using a dataset of 60 images are 0.862 and 0.944 respectively on a per patch basis and 1.00 and 0.90 respectively on a per image basis.     

A novel low false alarm rate pedestrian detection framework based on single depth images

Pedestrian detection is an important image understanding problem with many potential applications. There has been little success in creating an algorithm which exhibits a high detection rate while keeping the false alarm in a relatively low rate. This paper presents a method designed to resolve this problem. The proposed method uses the Kinect or any similar type of sensors which facilitate the extraction of a distinct foreground. Then potential regions, which are candidates for the presence of human(s), are detected by employing the widely used Histogram of Oriented Gradients (HOG) technique, which performs well in terms of good detection rates but suffers from significantly high false alarm rates. Our method applies a sequence of operations to eliminate the false alarms produced by the HOG detector based on investigating the fine details of local shape information. Local shape information can be identified by efficient utilization of the edge points which, in this work, are used to formulate the so called Shape Context (SC) model. The proposed detection framework is divided in four sequential stages, with each stage aiming at refining the detection results of the previous stage. In addition, our approach employs a pre-evaluation stage to pre-screen and restrict further detection results. Extensive experimental results on the dataset created by the authors, involves 673 images collected from 11 different scenes, demonstrate that the proposed method eliminates a large percentage of the false alarms produced by the HOG pedestrian detector.     

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.     

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。     

Comparative evaluation of linear edge detection methods

A comparative evaluation of the most commonly used linear methods for edge detection in grayscale images are presented. Detectors based on the first and second derivatives of image brightness are considered. The method for automatic edge tracking in grayscale images is proposed. The model for assessing errors and artifacts caused by sampling during digitization of real input images is proposed. Investigation of edge detectors isotropy and errors caused by input images sampling is conducted. The advantage of the Isotropic operator for edge tracking is shown. The noise immunity of linear edge detection methods is assessed and the superiority of 3 × 3 gradient operators for noisy images is shown. Isotropic and Sobel operators are identified to be optimal on a basis of sampling errors, output noise level, and computational complexity.     

基于冲突再分配DSmT的多尺度融合边缘检测算法

由于实际景象地物特征复杂,单一尺度边缘检测算子提取的边缘与噪声点测度差异小,因此将导致细小地物与噪声相互掺杂,边缘提取不准确的现象。针对此问题,提出了一种基于冲突再分配DSmT的多尺度融合边缘检测算法。首先提取图像多尺度边缘测度,接着提出双向指数映射基本置信指派构造方法构造多尺度边缘测度基本置信指派,然后采用冲突再分配DSmT组合规则对多尺度边缘置信指派进行融合,最后根据融合后的边缘置信指派图通过双阈值法确定边缘像素。通过对可见光和合成孔径雷达（SAR）图像的仿真实验表明,该算法相比单一尺度的Canny算子在边缘提取过程减小了误检和漏检边缘点数目,在抑制噪声的同时,大量保留了景象细节信息。     

A method of the wavelet transformation for estimation of geometrical parameters upon the diagnostic images

A problem of analyzing blood vessels on retinal images is considered. We suggest a method for extracting vessels using a wavelet transform. The wavelet transform is modified by adding a wavelet rotation parameter and normalization with respect to the signal energy within the wavelet carrier limits. The new methods, for constructing the vessel central lines based on the modified wavelet transform, are being used for vessels geometrical parameters estimation. The parameters of vessels that are located at the edge of the optic nerve disk are then used while diagnostic criteria formation for eye-ground pathological changes determining.     

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.     

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。     

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.

     

Segmentation of retinal blood vessels using the radial projection and semi-supervised approach

Automatic segmentation of retinal blood vessels has become a necessary diagnostic procedure in ophthalmology. The blood vessels consist of two types of vessels, i.e., thin vessels and wide vessels. Therefore, a segmentation method may require two different processes to treat different vessels. However, traditional segmentation algorithms hardly draw a distinction between thin and wide vessels, but deal with them together. The major problems of these methods are as follows: (1) If more emphasis is placed on the extraction of thin vessels, the wide vessels tend to be over detected; and more artificial vessels are generated, too. (2) If more attention is paid on the wide vessels, the thin and low contrast vessels are likely to be missing. To overcome these problems, a novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method is presented in this paper. The radial projection method is used to locate the vessel centerlines which include the low-contrast and narrow vessels. Further, we modify the steerable complex wavelet to provide better capability of enhancing vessels under different scales, and construct the vector feature to represent the vessel pixel by line strength. Then, semi-supervised self-training is used for extraction of the major structures of vessels. The final segmentation is obtained by the union of the two types of vessels. Our approach is tested on two publicly available databases. Experiment results show that the method can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions.     

Cell image segmentation using bacterial foraging optimization

Edge detection is the most commonly used method for cell image segmentation, where local search strategies are employed. Although traditional edge detectors are computationally efficient, they are heavily reliant on initialization and may produce discontinuous edges. In this paper, we propose a bacterial foraging-based edge detection (BFED) algorithm to segment cell images. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations that mimic the behavior of Escherichia coli. Our nature-inspired evolutionary algorithm, can identify the desired edges and mark them as the tracks of bacteria. We have evaluated our algorithm against four edge detectors − the Canny, SUSAN, Verma's and an active contour model (ACM) technique − on synthetic and real cell images. Our results indicate that the BFED algorithm identifies boundaries more effectively and provides more accurate cell image segmentation.