A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> We present a method for retinal blood vessel segmentation based upon the scale-space analysis of the first and second derivative of the intensity image which gives information about its topology and overcomes the problem of variations in contrast inherent in these images. We use the local maxima over scales of the magnitude of the gradient and the maximum principal curvature as the two features used in a region growing procedure. In the first stage, the growth is constrained to regions of low gradient magnitude. In the final stage this constraint is relaxed to allow borders between regions to be defined. The algorithm is tested in both red-free and fluorescein retinal images. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> Color retinal photography is an important tool to detect the evidence of various eye diseases. Novel methods to extract the main features in color retinal images have been developed in this paper. Principal component analysis is employed to locate optic disk; A modified active shape model is proposed in the shape detection of optic disk; A fundus coordinate system is established to provide a better description of the features in the retinal images; An approach to detect exudates by the combined region growing and edge detection is proposed. The success rates of disk localization, disk boundary detection, and fovea localization are 99%, 94%, and 100%, respectively. The sensitivity and specificity of exudate detection are 100 % and 71 %, correspondingly. The success of the proposed algorithms can be attributed to the utilization of the model-based methods. The detection and analysis could be applied to automatic mass screening and diagnosis of the retinal diseases. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> 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 <s> BIB003 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> We present a general framework for vessel segmentation in retinal images with a particular focus on small vessels. The retinal images are first processed by a nonlinear diffusion filter to smooth vessels along their principal direction. The vessels are then enhanced using a compound vessel enhancement filter that combines the eigenvalues of the Hessian matrix, the response of matched filters, and edge constraints on multiple scales. The eigenvectors of the Hessian matrix provide the orientation of vessels and so only one matched filter is necessary at each pixel on a given scale. This makes the enhancement filter is more efficient compared with existing multiscale matched filters. Edge constraints are used to suppress the response of spurious boundary edges. Finally, the center lines of vessels are tracked from seeds obtained using multiple thresholds of the enhanced image. Evaluation of the enhancement filter and the segmentation is performed on the publicly available DRIVE database. <s> BIB004 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> 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. <s> BIB005 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> This paper presents an efficient method for automatic extraction of blood vessels in retinal images to improve the detection of low contrast and narrow vessels. The proposed algorithm is composed of four steps: curvelet-based contrast enhancement, match filtering, curvelet-based edge extraction, and length filtering. In this base, after reconstruction of enhanced image from the modified curvelet coefficients, match filtering is used to intensify the blood vessels. Then we employ curvelet transform to segment vessels from its background and finally the length filtering is used to remove the misclassified pixels. The performance of algorithm is evaluated on DRIVE [1] databases and compared with those obtained from a hand-labeled ground truth. Since the curvelet transform is well-suited to handle curve discontinuities, we achieve an area under ROC curve of 0.9631 that demonstrates improved performance of proposed algorithm compared with known techniques. <s> BIB006 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> In this paper an algorithm for vessel segmentation and network extraction in retinal images is proposed. A new multi-scale line-tracking procedure is starting from a small group of pixels, derived from a brightness selection rule, and terminates when a cross-sectional profile condition becomes invalid. The multi-scale image map is derived after combining the individual image maps along scales, containing the pixels confidence to belong in a vessel. The initial vessel network is derived after map quantization of the multi-scale confidence matrix. Median filtering is applied in the initial vessel network, restoring disconnected vessel lines and eliminating noisy lines. Finally, post-processing removes erroneous areas using directional attributes of vessels and morphological reconstruction. The experimental evaluation in the publicly available DRIVE database shows accurate extraction of vessels network. The average accuracy of 0.929 with 0.747 sensitivity and 0.955 specificity is very close to the manual segmentation rates obtained by the second observer. The proposed algorithm is compared also with widely used supervised and unsupervised methods and evaluated in noisy conditions, giving higher average sensitivity rate in the same range of specificity and accuracy, and showing robustness in the presence of additive Salt&Pepper or Gaussian white noise. <s> BIB007 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> 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. <s> BIB008 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection. <s> BIB009 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> 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. <s> BIB010 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> 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. <s> BIB011 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a postprocessing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy. <s> BIB012 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> A variety of blood vessel extraction (BVE) techniques exist in the literature, but they do not always lead to acceptable solutions especially in the presence of anomalies where the reported work is limited. Four techniques are presented for BVE: (1) BVE using Image Line Cross-Sections (ILCS), (2) BVE using Edge Enhancement and Edge Detection (EEED), (3) BVE using Modified Matched Filtering (MMF), and (4) BVE using Continuation Algorithm (CA). These four techniques have been designed especially for abnormal retinal images containing low vessel contrasts, drusen, exudates, and other artifacts. The four techniques were applied to 30 abnormal retinal images, and the success rate was found to be (95 to 99%) for CA, (88–91%) for EEED, (80–85%) for MMF, and (74–78%) for ILCS. Application of these four techniques to 105 normal retinal images gave improved results: (99-100%) for CA, (96–98%) for EEED, (94-95%) for MMF, and (88–93%) for ILCS. Investigations revealed that the four techniques in the order of increasing performance could be arranged as ILCS, MMF, EEED, and CA. Here we demonstrate these four techniques for abnormal retinal images only. ILCS, EEED, and CA are novel additions whereas MMF is an improved and modified version of an existing matched filtering technique. CA is a promising technique. <s> BIB013 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> Measurements of retinal blood vessel mo rphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis Both the arteries and veins of the retina are generally binary trees, whose properties can be considered either locally or g lobally. Measurable geometrical changes in diameter, branching angle, length, or tortuosity, as a result of disease, have been described in retinal blood vessels. The detection and measurement of retinal blood vessels can be used to quantify the severity of disease such as hypertension, stroke and arteriosclerosis, as part of the process of automated diagnosis of disease or in the assessment of the progression of therapy. Thus, a reliable method of vessel detection and quantification would be valuable. In this paper, we address the problem of identifying true vessels as a postprocessing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. <s> BIB014 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Vessel Extraction of Fundus Retinal Image. <s> Retinal vessel tree extraction is a crucial step for analyzing the microcirculation, a frequently needed process in the study of relevant diseases. To date, this has normally been done by using 2D image capture paradigms, offering a restricted visualization of the real layout of the retinal vasculature. In this work, we propose a new approach that automatically segments and reconstructs the 3D retinal vessel tree by combining near-infrared reflectance retinography information with Optical Coherence Tomography (OCT) sections. Our proposal identifies the vessels, estimates their calibers, and obtains the depth at all the positions of the entire vessel tree, thereby enabling the reconstruction of the 3D layout of the complete arteriovenous tree for subsequent analysis. The method was tested using 991 OCT images combined with their corresponding near-infrared reflectance retinography. The different stages of the methodology were validated using the opinion of an expert as a reference. The tests offered accurate results, showing coherent reconstructions of the 3D vasculature that can be analyzed in the diagnosis of relevant diseases affecting the retinal microcirculation, such as hypertension or diabetes, among others. <s> BIB015
Extracting the vessel tree of retinal microvasculature is an important step to analyze the microcirculation for the detection of retinal diseases. The study of BIB001 reported a retinal blood vessel segmentation algorithm following the scale-space analysis of the first and second derivative of the intensity image. A parallel multiscale feature extraction and region growing algorithm based on ITK (Insight Segmentation and Registration Toolkit) was developed for retinal blood vessel segmentation and showed that this method is effective for high-resolution retinal image analysis  . The study of BIB015 designed an automated segmentation and reconstruction method for 3-dimensional retinal vessel tree extraction, vessel detection, and vessel calibers estimation assembling the near-infrared reflectance retinography information with OCT section. A postprocessing model for extracting features of fundus retinal image was introduced in BIB012 BIB014  that segment the retinal microvasculature to extract the blood vessel applying Krisch Edge Detector and identify the true vessel using Graph Tracer. An automated segmentation method was developed in  for segmenting the image of retinal microvascular structure to identify the true vessel using pixel's feature vectors that are the combination of pixel's intensity and continuous two-dimensional Morelet Wavelet transform response adopted at multiple scales. The study of BIB004 proposed a vessel segmentation technique using nonlinear diffusion filter for the smoothening vessel to their principal direction, compound vessel enhancement filter combining eigenvalues of the Hessian matrix, matched filter response, and edge constraints of multiple scales for vessel enhancement and then multiple thresholding was applied for centerline tracking. The study of BIB008 introduced an automatic system to enhance and segment true blood vessel of fundus retinal image using 2-dimensional Gabor wavelet and multilayered thresholding, respectively. The authors of BIB010 BIB003 combined several techniques for the segmentation of fundus retinal image that can detect the vessel centerline and slice the morphological bit plane to extract the vessel tree of the human retina. The study of  presented two different methods for retinal blood vessel segmentation where the first method is the procedure for region growing using the hysteresis thresholding which later applied to the response vector similarities of adjacent pixels within the fundus image and the second method was developed based on region growing and directional response vector similarities. For the latter method response vector was calculated through template matching with general Gabor function  . The study of BIB007 proposed an algorithm for vessel segmentation and vascular network extraction based on the multiscale line-tracking  procedure where map quantization of the multiscale confident matrix was applied to generate the initial vessel network and then disconnected vessel line was restored and noisy line eliminated applying the median filtering to the generated vascular network and finally directional attributes of vessel and morphological reconstruction was applied as postprocessing for avoiding the faulty areas. An automatic blood vessel extraction technique was presented in BIB006 in which curvelet-based contrast enhancement, match filtering, curvelet-based edge extraction, and length filtering were used to extract the blood vessel. The study of BIB011 proposed a vessel segmentation method to detect the blood vessel in the retinal image based on a probabilistic tracking method. The study of  presented a multiconcavity-based segmentation model that deals with the lesion in retinal microvasculature. There were four different techniques demonstrated in BIB013 for blood vessel segmentation based on Edge Enhancement Edge Detection, ImageLine Cross-Section, Continuation Algorithm, and Modified Matched Filtering that can work with abnormal retinal images having exudates, drusen, and low vessel contrast. A supervised method for detecting blood vessel in fundus retinal images was designed in BIB009 based on a blueprint of NN for pixel classification and a 7D vector computation that is composed of moment-invariant based features for pixel representation and grey-level. The study of BIB002 examined a novel method for feature extraction of color retinal image applying Principal Component Analysis, Active Shape Model, fundus coordinates system, and a combined region growing and edge detection technique to locate optic disc, detect the shape of an optic disc, describe the features, and detect the exudates in color retinal image, respectively. The blood vessels in a fundus retinal image that appear in a higher illumination variance area are found missing if the background removal method for segmentation is applied because the background and intensity values of the blood vessel are almost the same. The study of  presented a robust method for blood vessel segmentation to change the illumination intensity applying background estimation that was calculated by a weighted surface fitting method with a higher degree polynomial. ensure the effectiveness of the developed systems for clinical diagnosis of diabetic and hypertensive retinopathy, the vessel identification must be accurate. The authors of BIB005 designed an automated method for the artery and vein identification in dual-wavelength, 570nm and 600nm, retinal images. In this system they utilized the structural feature, relative strength of the vessel central reflex, and the ratio of the vessel optical densities from images at oxygen-sensitive and oxygen-insensitive wavelengths as the functional feature for each vessel segment to differentiate the artery from vein. The study of BIB005 employed the dual-Gaussian model in which parameter was estimated using a robust M-estimator to compute the relative strength of the central reflex. For the identification of vessel type whether it is artery or vein, the structural and functional features were combined in four classifiers and it was observed that the Support Vector Machine gave the best result with 97% and 90% positive rates for bot arteries and veins, respectively BIB005 .