基于局部期望阈值分割的图像边缘检测算法

传统图像边缘特征检测通过梯度算子卷积计算获取梯度图,并根据梯度变化情况设定阈值得到边缘信息,但图像的各局部区域梯度变化不均匀,采用统一阈值分割边缘信息往往会造成获取的边缘信息不准确。本文提出一种基于图像局部区域期望的自适应阈值方法,首先采用Sobel算子获取图像梯度矩阵,然后将梯度矩阵分割为多个子区域,并计算每个子区域的局部期望作为该区域阈值,进行边缘特征提取。实验表明,提出的方法提高了图像主要目标物边缘特征的识别度,区域边缘信息划分准确。     

基于分数阶微分增强的肺CT图像血管分割

为了提高对肺CT图像中血管自动分割的准确性,提出基于分数阶微分增强的局部子区域分割方法。通过对肺CT图像的增强、分割方法和分数阶微分对图像细微细节的增强能力的比较和研究后, 该方法先采用构建的分数阶微分算子对肺CT图像加以增强后, 再用两个控制指标获取的局部区域最优阈值来分割肺血管。实验结果表明, 它可以有效地提取肺血管网络并且能够分割得到更为丰富的血管细节; 对比传统方法的肺血管分割结果,它能更准确地分割出肺CT图像中的血管。     

基于眼底图像层次特征的分类方法

针对眼底图像中视网膜血管结构的划分问题,提出一种自适应的广度优先搜索算法。首先,基于视网膜血管的结构提出层次特征的概念并进行特征提取;然后,对分割的视网膜血管进行分析及处理,提取得到多个无向图子图;最后,使用自适应的广度优先搜索算法对每个子图中的层次特征进行分类。视网膜血管结构的划分问题被转化为层次特征的分类问题,通过对视网膜血管中的层次特征进行分类,包含这些层次特征的视网膜血管段的层次结构就可以被确定,从而实现视网膜血管结构的划分。该算法运用于公开的眼底图像数据库时具有良好的性能。     

基于图像的颗粒物检测研究

针对复杂背景下的显微图像中的颗粒物检测,提出了一种快速、准确的颗粒图像分割新方法;该方法利用Canny算子得到图像中的颗粒边缘,采用格雷厄姆法计算颗粒边缘的凸壳,得到局部闭合区间;通过对单个闭合计区间内像素值信息的统计,得到局部阈值,利用局部阈值进行阈值分割得到颗粒二值图像;实验结果表明该方法有效克服了背景中纹理的影响,准确地分割出了颗粒图像,基于该算法开发的接触式表面洁净度检测仪具有较高的检测精度,得到了较好的应用。     

基于改进分水岭算法的VCH-F1图像分割

针对分水岭算法存在的过分割问题以及VCH-F1切片图像的特点,提出了一种能够有效消除局部极小值和噪声干扰的分割方法。首先比较并选取彩色分量图像梯度信息的最大值,达到提取图像有效边缘信息的目的;然后提出基于多阈值分割的方法消除无效梯度信息;最后介绍了算法的步骤及结果。实验结果证明,基于该方法处理梯度图像进行分水岭算法处理可以得到准确的分割结果。     

基于微粒群算法的视网膜血管自动提取方法

针对视网膜血管网络灰度分布特征与结构特征,提出了将灰度-梯度共生矩阵最大熵与微粒群算法相结合的视网膜血管提取方法。采用Gabor滤波以增强血管图像,获取增强后视网膜图像的灰度-梯度共生矩阵,利用微粒群算法并结合灰度-梯度共生矩阵的最大熵方法进行阈值化处理,对图像进行二值化处理后根据视网膜血管具有区域连通性的特征,采用形态学方法分割出最终的血管。实验结果表明,该方法能有效地提取视网膜血管网络。     

基于Gabor小波的视网膜血管自动提取研究

针对视网膜血管网络灰度分布特征和区域结构特征,提出了一种基于Gabor小波的视网膜血管提取方法。采用Gabor滤波预处理以增强血管,用改进的自适应二值化方法对增强后的视网膜图像进行二值化处理,根据视网膜血管具有区域连通性的特征,并用形态学方法分割出最终的血管。为验证方法的有效性,对Hoover眼底图像库进行实验,结果表明该方法在细小血管的提取以及连续性、有效性方面都优于Hoover算法。     

基于融合相位特征的视网膜血管分割算法

针对相位一致性特征对血管中心检测不足问题,提出基于融合相位特征的眼底视网膜血管分割算法。首先,预处理原始的视网膜图像;然后,对图像中每个像素构造4D的特征向量（包括Hessian矩阵、Gabor变换、条带选择组合位移滤波响应（B-COSFIRE）滤波、相位特征）;最后,采用支持向量机（SVM）进行像素分类,实现眼底视网膜血管的分割。其中,相位特征是将分别提取的相位一致性特征与Hessian矩阵特征进行小波融合后得到的一种新的融合相位特征。该特征既保留了相位一致性特征良好的血管边缘信息,又克服了相位一致性特征对血管中心检测的不足。在用于血管提取的数字视网膜图像（DRIVE）数据库上测得基于融合相位特征的视网膜血管分割算法的平均准确率（Acc）为0.9574,平均受试者工作曲线面积（AUC）为0.9702;且在单一特征进行像素分类提取血管的实验中,与使用相位一致性特征相比,使用融合相位特征进行像素分类提取血管的Acc由0.9191提高到0.9478,AUC由0.9359提高到0.9578。实验结果表明,融合相位特征比相位一致性特征更适用于基于像素分类的眼底视网膜血管分割算法。     

一种基于边缘检测的局部阀值分割算法

本文提出了一种基于边缘检测的局部阈值分割方法，该方法将整睛灰度图像分成小块，在每个小块中利用梯度算子对小块中的边界点进行检测，寻找出小块内的所有边界点，然后沿着这些边界点的梯度方向找出最临近的点，以所有这些临近点和边界点的灰度均值作为该小块的灰度阈值进行分割，该算法计算复杂度较低，避开了灰度直方图阈值分割方法中“谷底”难以确定的问题，同时照顾到了图像的局部灰度特性。     

基于SC特征的叶片图像识别研究

提出一种基于改进SC形状上下文描述子的叶片图像特征提取方法。利用颜色聚类分割图像,使用Ostu算子实现二值化处理,提取图像边缘轮廓,结合形状上下文(SC)描述子提取图像轮廓特征,计算匹配代价矩阵,利用匈牙利算法获得最小匹配代价。结果表明该算法具有较高的识别准确度。     

Retinal vessel extraction using dynamic multi-scale matched filtering and dynamic threshold processing based on histogram fitting

Automatic extraction of retinal vessels is of great significance in the field of medical diagnosis. Unfortunately, extracting vessels in retinal images with uneven background is a challenging task. In addition, accurate extraction of vessels with different widths is difficult. Aiming at these problems, in this paper, a new dynamic multi-scale filtering method together with a dynamic threshold processing scheme was proposed. The image is first divided into sub-images to facilitate the analysis of gray features. Then for each sub-image, the scales of the matched filter and the segmentation threshold are dynamically determined in accordance with the Gaussian fitting results of the gray distribution. Compared with the current blood vessel extraction algorithms based on multi-scale matched filter using uniform scales for the whole retinal image, the proposed method detects many fine vessels drowned by noise and avoids an overestimation of the thin vessels while improving the accuracy of segmentation in general.     

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.

     

Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses

Automated segmentation of blood vessels in retinal images can help ophthalmologists screen larger populations for vessel abnormalities. However, automated vessel extraction is difficult due to the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable. Further, the small vessels are overwhelmed by Gaussian-like noises. Therefore the accurate segmentation and width estimation of small vessels are very challenging. In this paper, we propose a simple and efficient multiscale vessel extraction scheme by multiplying the responses of matched filters at three scales. Since the vessel structures will have relatively strong responses to the matched filters at different scales but the background noises will not, scale production could further enhance vessels while suppressing noise. After appropriate selection of scale parameters and appropriate normalization of filter responses, the filter responses are then extracted and fused in the scale production domain. The experimental results demonstrate that the proposed method works well for accurately segmenting vessels with good width estimation.     

基于DWT和形态学的眼底图像小血管检测

针对眼底图像中末端小血管检测难、细节容易丢失的问题．提出一种基于离散小波变换（DWT）和形态学滤波的检测算法。通过小波变换多尺度分析眼底图像小血管系数、背景系数的不同特征．选取分量信号的系数后重构图像。同时以自适应阈值Canny算法提取小血管边缘;然后将结合小血管宽度选择适当结构元素半径,对重构图像进行灰度膨胀,实现小血管检测。结果表明,形态学结合DWT的检测算法能够准确地检测小血管．与常见边缘检测算法相比检测成功率较高。     

Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images

In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of interest. In this sense, our approach is regarded as knowledge-guided adaptive thresholding, in contrast to most algorithms known from the literature. We apply our general framework to detect vessels in retinal images. An experimental evaluation demonstrates superior performance over global thresholding and a vessel detection method recently reported in the literature. Due to its simplicity and general nature, our novel approach is expected to be applicable to a variety of other applications.     

Detection and Classification of Hemorrhages in Retinal Images

Damage of the blood vessels in retina due to diabetes is called diabetic retinopathy (DR). Hemorrhages is the first clinically visible symptoms of DR. This paper presents a new technique to extract and classify the hemorrhages in fundus images. The normal objects such as blood vessels, fovea and optic disc inside retinal images are masked to distinguish them from hemorrhages. For masking blood vessels, thresholding that separates blood vessels and background intensity followed by a new filter to extract the border of vessels based on orientations of vessels are used. For masking optic disc, the image is divided into sub-images then the brightest window with maximum variance in intensity is selected. Then the candidate dark regions are extracted based on adaptive thresholding and top-hat morphological techniques. Features are extracted from each candidate region based on ophthalmologist selection such as color and size and pattern recognition techniques such as texture and wavelet features. Three different types of Support Vector Machine (SVM), Linear SVM, Quadratic SVM and Cubic SVM classifier are applied to classify the candidate dark regions as either hemorrhages or healthy. The efficacy of the proposed method is demonstrated using the standard benchmark DIARETDB1 database and by comparing the results with methods in silico. The performance of the method is measured based on average sensitivity, specificity, F-score and accuracy. Experimental results show the Linear SVM classifier gives better results than Cubic SVM and Quadratic SVM with respect to sensitivity and accuracy and with respect to specificity Quadratic SVM gives better result as compared to other SVMs.     

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.     

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.     

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 vessel segmentation using a probabilistic tracking method

Vessel structures such as retinal vasculature are important features for computer-aided diagnosis. In this paper, a probabilistic tracking method is proposed to detect blood vessels in retinal images. During the tracking process, vessel edge points are detected iteratively using local grey level statistics and vessel's continuity properties. At a given step, a statistic sampling scheme is adopted to select a number of vessel edge points candidates in a local studying area. Local vessel's sectional intensity profiles are estimated by a Gaussian shaped curve. A Bayesian method with the Maximum a posteriori (MAP) probability criterion is then used to identify local vessel's structure and find out the edge points from these candidates. Evaluation is performed on both simulated vascular and real retinal images. Different geometric shapes and noise levels are used for computer simulated images, whereas real retinal images from the REVIEW database are tested. Evaluation performance is done using the Segmentation Matching Factor (SMF) as a quality parameter. Our approach performed better when comparing it with Sun's and Chaudhuri's methods. ROC curves are also plotted, showing effective detection of retinal blood vessels (true positive rate) with less false detection (false positive rate) than Sun's method.