A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> The change in diameter of retinal vessels as a function of increasing distance to the optic disc is believed to be indicative of the risk level of various vascular diseases such as generalised arteriosclerosis and Diabetes Mellitus. In particular, focal arteriolar narrowing (FAN) is considered related to arteriosclerosis. The aim of this work is to develop methods to provide quantitative information about the FAN status of retinal arteriolar vessel segments. We propose a method to measure the vessel diameter and changes herein along the vessel. The width or diameter measurement is based on intensity profiles orthogonal to a smoothed trace in the vessel found by Dijkstra’s shortest path algorithm. The vessel diameter is calculated from the intensity profiles using two diAerent methods to estimate profile widths. We propose a ‘‘normalised accumulated gradient’’ (NAG) and the coeAcient of variance (CV) to estimate the FAN in a vessel segment. The NAG is designed to detect increases in the vessel diameter as the distance to the papilla increases. The performance of the methods developed is compared to the evaluation by a skilled ophthalmologist. The methods are seen to perform well. ” 2000 Elsevier Science B.V. All rights reserved. <s> BIB001 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> AIMS—Quantification of retinal vascular change is difficult and manual measurements of vascular features are slow and subject to observer bias. These problems may be overcome using computer algorithms. Three automated methods and a manual method for measurement of arteriolar diameters from digitised red-free retinal photographs were compared. ::: METHODS—60 diameters (in pixels) measured by manual identification of vessel edges in red-free retinal images were compared with diameters measured by (1) fitting vessel intensity profiles to a double Gaussian function by non-linear regression, (2) a standard edge detection algorithm (Sobel), and (3) determination of points of maximum intensity variation by a sliding linear regression filter (SLRF). Method agreement was analysed using Bland-Altman plots and the repeatability of each method was assessed. ::: RESULTS—Diameter estimations obtained using the SLRF method were the least scattered although diameters obtained were approximately 3 pixels greater than those measured manually. The SLRF method was the most repeatable and the Gaussian method less so. The Sobel method was the least consistent owing to frequent misinterpretation of the light reflex as the vessel edge. ::: CONCLUSION—Of the three automated methods compared, the SLRF method was the most consistent (defined as the method producing diameter estimations with the least scatter) and the most repeatable in measurements of retinal arteriolar diameter. Application of automated methods of retinal vascular analysis may be useful in the assessment of cardiovascular and other diseases. <s> BIB002 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> PURPOSE: To determine, with novel software, the feasibility of measuring the tortuosity and width of retinal veins and arteries from digital retinal images of infants at risk of retinopathy of prematurity (ROP). METHODS: The Computer-Aided Image Analysis of the Retina (CAIAR) program was developed to enable semiautomatic detection of retinal vasculature and measurement of vessel tortuosity and width from digital images. CAIAR was tested for accuracy and reproducibility of tortuosity and width measurements by using computer-generated vessel-like lines of known frequency, amplitude, and width. CAIAR was then tested by using clinical digital retinal images for correlation of vessel tortuosity and width readings compared with expert ophthalmologist grading. RESULTS: When applied to 16 computer-generated sinusoidal vessels, the tortuosity measured by CAIAR correlated very well with the known values. Width measures also increased as expected. When the CAIAR readings were compared with five expert ophthalmologists' grading of 75 vessels on 10 retinal images, moderate correlation was found in 10 of the 14 tortuosity output calculations (Spearman rho = 0.618-0.673). Width was less well correlated (rho = 0.415). CONCLUSIONS: The measures of tortuosity and width in CAIAR were validated using sequential model vessel analysis. On comparison of CAIAR output with assessments made by expert ophthalmologists, CAIAR correlates moderately with tortuosity grades, but less well with width grades. CAIAR offers the opportunity to develop an automated image analysis system for detecting the vascular changes at the posterior pole, which are becoming increasingly important in diagnosing treatable ROP. <s> BIB003 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> Background: Lacunar strokes account for 25% of ischaemic strokes, but their precise aetiology is unknown. Similarities between the retinal and cerebral small vessels mean that clarification of the exact relationship between retinal microvascular abnormalities and stroke, and particularly with stroke subtypes, may aid understanding of the aetiology of lacunar stroke and stroke risk. Methods: A systematic review of the literature was performed by searching Medline and Embase to October 2007 for studies in humans that investigated the association between retinal microvascular abnormalities and prevalent or incident stroke. Data and calculated summary risk ratios (sRR) were extracted for associations between retinal microvascular abnormalities and stroke, including stroke subtypes where possible, adjusted for key variables. 37 papers from 22 different studies were included with 62 975 subjects (mean age 62 years) among whom there were 2893 strokes. Stroke identification and diagnosis methods varied. Results: Retinopathy was associated with incident stroke (sRR 2.1, 95% CI 1.7 to 2.6) and prevalent stroke (sRR 2.5, 95% CI 1.4 to 4.3). Incident stroke was also associated with retinal artery embolism (sRR 2.9, 95% CI 1.6 to 5.1) and venular widening (sRR 1.4, 95% CI 1.1 to 1.7). There was significant heterogeneity between studies for some associations. There were no data on retinal microvascular abnormalities and haemorrhagic versus ischaemic stroke or ischaemic stroke subtypes. Conclusions: Retinal microvascular abnormalities are associated with stroke, but more data are required to clarify associations between specific types of retinal microvascular abnormality and stroke, as well as between different stroke subtypes. Future retinal-stroke studies should concentrate on carefully diagnosing and accurately sub-typing ischaemic stroke. <s> BIB004 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> 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. <s> BIB005 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, three-dimension surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. Two datasets from the REVIEW database were used to assess the performance of proposed method. This algorithm is robust and is able to produce accurate measurements. <s> BIB006 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> This paper proposes an algorithm to measure the width of retinal vessels in fundus photographs using graph-based algorithm to segment both vessel edges simultaneously. First, the simultaneous two-boundary segmentation problem is modeled as a two-slice, 3-D surface segmentation problem, which is further converted into the problem of computing a minimum closed set in a node-weighted graph. An initial segmentation is generated from a vessel probability image. We use the REVIEW database to evaluate diameter measurement performance. The algorithm is robust and estimates the vessel width with subpixel accuracy. The method is used to explore the relationship between the average vessel width and the distance from the optic disc in 600 subjects. <s> BIB007 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> Image quality analysis is to study the quality of images and develop methods to efficiently and swiftly determine the quality of images. It is an important process especially in this digital age whereby transmission, compression and conversion are compulsory. Therefore, this paper proposed a hybrid method to determine the image quality by using Levenberg-Marquardt Back-Propagation Neural Network (LMBNN). Three known quality metrics were combined as the input element to the network. A proper set of network properties was chosen to represent this element and was trained using Levenberg-Marquardt algorithm (trainlm) in MATLAB. From the preliminary simulation, a promising output result was obtained indicated by good performance metrics results and good regression fitting. <s> BIB008 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> Study of retinal blood vessel features, especially the caliber, has been widely used for risk level assessment of certain disease conditions. In this work a vessel diameter measurement technique based on Gaussian modeling has been proposed. This method adaptively combines a series of second and higher order Gaussians to model the vessel profile and uses the sigma parameter of the best fitted Generalized Gaussians to the boundaries, to measure the vessel caliber. One advantage of this technique is that, unlike other methods, it does not assume the vessel profile as a symmetrical Gaussian-like shape with similar blurriness levels at the edges. The technique has been tested on 580 cross-sections from normal and pathological public datasets with mixed quality fundus images. Comparison was made against two gold standard techniques and a ground truth obtained manually by three experts to sub-pixel accuracy. <s> BIB009 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> An automatic vessel diameter measurement technique based on linear discriminant analysis (LDA) has been proposed. After estimating the vessel wall, the vessel cross-section profile is divided into three regions: two corresponding to the background and one to the vessel. The algorithm was tested on more than 5000 cross-sections of retinal vessels from the REVIEW dataset through comparative study with the state-of-the-art techniques. Cross-correlation analyses were performed to determine the degree to which the proposed technique was close to the ground truth. The results indicate that proposed algorithm consistently performed better than most of other techniques and was highly correlated with the manual measurement as the reference diameter. The proposed method does not require any supervision and is suitable for automatic analysis. <s> BIB010 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> This paper focuses on evaluating a method for measuring the retinal vessel widths in a recently publicly available database (REVIEW). The algorithm is based on deformable models and it constitutes an essential step in a methodology to compute the AVR automatically. This methodology is part of a web-based framework aimed to integrate diverse techniques in the field of retinal image analysis. The results achieved in the REVIEW database indicate the method is comparable or, in some cases, improves the accuracy of other techniques for measuring the vessel widths. <s> BIB011 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> Abstract We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests. An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy. <s> BIB012 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> A retinal image can establish the state of human body by simply providing the information about what is happening inside it. Because the AVR status of blood vessels is having good correlation with hypertension, coronary, heart disease and stroke. However they require the accurate extraction of distinct blood vessels and width measurement. The Existing width measurement techniques met with a challenging problem due to ambiguities caused by bifurcations, crossovers and the mis-identification of vessel caliber. In this paper, Graph Tracer Algorithm is introduced to deal with above measurement conflicts which means, it identify true blood vessels and its appropriate bifurcations and cross overs. This algorithm is a post processing step which is followed by the segmentation technique, Multi-Scale Line Tracking (MSLT). Finally Identified blood vessels are subjected into the width measurement with the help of vessel caliber annotation tool. The proposed measurement technique is giving 94.6% of accuracy and this method provides extra 15% of accuracy than the normal measuring technique. <s> BIB013 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> This paper proposes the use of an adaptive model to measure the width of retinal vessels in fundus photographs. It is based on the hypothesis that the Higuchi's dimension of the cross-section is proportional to the vessel diameter. This approach does not require image segmentation and binarization of the vessels and therefore is suitable for machine based measurements. The model is developed using a synthetic image of the vessel of known diameter and added noise. As a first step, the vessel cross-section profiles were manually obtained and analyzed using Higuchi's fractal dimension method. A 3D model was formed using the relationship between Higuchi's dimension, vessel diameter and noise variance. This model was then evaluated using expert annotated REVIEW public database and solved for the vessel diameter for performance evaluation. The result showed a good agreement with state of the art techniques. The model is background noise tolerant, estimates the vessel width with subpixel precision and does not require manual intervention. <s> BIB014 </s> A Review on the Extraction of Quantitative Retinal Microvascular Image Feature <s> Image Processing-Based Blood Vessel Diameter Measurement for Stroke Risk Detection <s> Several clinical studies reveal the relationship between alterations in the topologies of the human retinal blood vessel, the outcrop and the disease evolution, such as diabetic retinopathy, hypertensive retinopathy, and macular degeneration. Indeed, the detection of these vascular changes always has gaps. In addition, the manual steps are slow, which may be subjected to a bias of the perceiver. However, we can overcome these troubles using computer algorithms that are quicker and more accurate. This paper presents and investigates a novel method for measuring the blood vessel diameter in the retinal image. The proposed method is based on a thresholding segmentation and thinning step, followed by the characteristic point determination step by the Douglas-Peucker algorithm. Thereafter, it uses the active contours to detect vessel contour. Finally, Heron's Formula is applied to assure the calculation of vessel diameter. The obtained results for six sample images showed that the proposed method generated less errors compared to other techniques, which confirms the high performance of the proposed method. <s> BIB015
Vessel diameter was measured manually by selecting a region of 512 × 512 pixels of a digital image and then calculating the linear distance between two points of opposite edges of a vessel of a digital image. The Gaussian measurement of vessel diameter was performed by analyzing a set of vessel characteristic parameters. These parameters were determined by fitting a double Gaussian model to the intensity crosssection of the vessel. The function below illustrates the intensity cross-section of the vessel and a modified LevenbergMarquardt BIB008 least squares method was used to determine the parameters a1 to a5 and a7. The calculated vessel width would be 2.33a3 BIB002 . Sobel edge detection masks were applied to perform Sobel measurements identification of vessel edge position that determines the vessel edge position from the average position of maximum edge strength which was averaged over a threepixel window. Linear regression within sliding window filter was used to create Sliding Linear Regression Filter (SLRF) measurement identification of edge positions. The SLRF were used to measure the vessel diameter and it was seen that the performance of SLRF was greater than the manual measurement system. Several studies revealed that there was no association with an arteriolar width decreasing and incident stroke and prevalent stroke rather venular widening was associated with incident stroke BIB004  . AVR was also analyzed to determine the relation between AVR and incident stroke and prevalent stroke as the arteriolar and venular widening is responsible for changing the value of AVR. No association was found between incident stroke and AVR but the association between Figure 13 : Widened blood vessel of retina BIB006 . AVR and prevalent stroke was examined BIB004 . Figure 13 shows the widened vessel of retinal vasculature. The researches of  developed an algorithm for measuring the diameter of the retinal blood vessel to subpixel accuracy by applying the two-dimensional difference of Gaussian model. The researchers came out with 30% more precision in comparison with Zhou's Gaussian model, Brinchmann-Hansen's half height and Gregson's rectangular profile and accuracy of a third of a pixel  . A semiautomatic vessel width measuring method named Computer-Aided Image Analysis of Retina (CAIAR) was formulated in BIB003 , in which computer-generated lines, similar to the blood vessel with prefixed frequency, amplitude, and width were used. The resultant width values of retinal blood vessel obtained from this formula were found as less correlated with the ophthalmologist grading BIB003 . The study of BIB006 proposed a graph-theoretic algorithm for measuring the vessel width. The work in BIB015 measures the blood vessel diameter based on thresholding segmentation and training step determining the characteristic point using DouglasPeucker algorithm. The work in BIB015 detected vessel contour using active contour and measured vessel diameter using Heron's formula. An algorithm based on the Graph Tracer method was developed in BIB013 , which can identify the true blood vessels, appropriate bifurcations, and crossover. Multiscale Line Tracing was applied for segmentation and blood vessel annotation tool was used to measure the width of the identified blood vessel and this technique achieved 94.6% accuracy in measuring the diameter of the blood vessel. The study of BIB001 proposed an algorithm for measuring vessel diameter based on intensity profiles and Dijkstra's shortest path algorithm. The work of  also presented a vessel diameter calculating method based on 2D modeling that is more precise than HHFM and Gregson algorithm. The study of BIB007 introduced a graph-based algorithm to measure the width of the retinal vessel that segmented both vessel edges following a two-slice, 3D surface segmentation problem model which was converted into a minimum closed set in a node-weighted graph problem in the next step. In their experiment it was shown that more accurate measurement of the vessel width of fundus images can be obtained with larger standard deviation ( ) and the success rates of this algorithm on four datasets, KPIS, CLRIS, VDIS, and HRIS, of REVIEW database are 99.4%, 94.1%, 96%, and 100%, respectively BIB007 . The authors of BIB009 proposed a vessel diameter measurement algorithm based on Gaussian modeling combining a series of second-order and higher order Gaussians to design the vessel profile and they used the sigma parameter of generalized Gaussians to the vessel boundaries. The accuracy and precision of this method for CLRIS are −1.574 and 1.691 and for VDIS are −0.443 and 1.182. They compared their result with twin-Gaussian, SLRF, and manual measurement and it was claimed that the accuracy and precision of twin-Gaussian are the least BIB009 . The authors of BIB014 designed a hypothesis based method for retinal vessel width measurement applying the theory that Higuchi's dimension of the cross-section is proportional to the vessel diameter. They used REVIEW database to validate their proposed method and came out with the precision and success rate of 0.65 and 99.45%, 1.56 and 98%, 0.45 and 100%, and 1.14 and 97.8% for HRIS, CLRIS, KPIS, and VDIS datasets, respectively. The advantage of this technique is that the results cannot be degraded by edge detection performance and segmentation process as it does not depends on segmentation process BIB014 . The study of BIB005 shaped an active contour model named Extraction of Segments Profile (ESP) to measure the retinal blood vessel diameter. Though their algorithm may fail sometimes, they obtained the success rates 99.7%, 99.6%, 93%, and 100% for HRIS, VDIS, CLRIS, and KPIS datasets, respectively, of REVIEW database BIB005 . An automated method for vessel diameter measurement based on LDA was developed in BIB010 unsupervised method, which can measure the vessel diameter to subpixel accuracy for all datasets. The study of BIB011 proposed an algorithm for measuring the retinal vessels widths based on deformable models and which was integrated into an AVR computing framework. This method was robust against different grey color spaces. The authors of BIB012 used a novel parametric surface model of the cross-sectional intensities of vessel and combination of bagged decision trees in their algorithm to estimate the retinal vessel width in fundus images. This algorithm was compared with several algorithms such as 1D Gaussian, 2D Gaussian, Gregson, HHFW, Extraction of Segment Profiles (ESP) of Al-Diri, Unsupervised Linear Discriminant Analysis based algorithm of Kumar et al., and graph-based algorithm of Xu et al. and came out with good stability, 100% success rate on all four datasets of REVIEW database BIB012 . Table 2 displays the performance in terms of accuracy and the applied method to shape the most recent algorithm for measuring the diameter of retinal blood vessels In Table 2 , the most widely used performance measurement of their proposed algorithm is success rate. The success rate can be defined as the ratio of the number of successful runs and the total number of runs. It is a measure of the stability of measurement that was returned BIB005 , along with the mean and standard deviation of measurements and differences. From Table 2 it is seen that the REVIEW database was used in most of the researches, whereas the success rate of the experiment using KPIS datasets achieved 100% with the better standard deviation in maximum cases. This is because the images of this dataset were taken from the clean, large vessel segments and downsampled such as HRIS BIB005 . Among the applied methods described here, the BIB012 's algorithm based on supervised learning performed by bagged decision trees and an extended multiresolution Hermite model showed the 100% success rate, maximum stability with a good standard deviation of the point to point difference for all the four datasets of REVIEW database. The graphtheoretic method of BIB006 BIB007 , ESP algorithm of BIB005 , Adaptive Higuchi's Dimension based algorithm of BIB014 , ULDM of BIB010 , and deformable model-based algorithm of BIB011 exhibited poor accuracy as the mean diameter of retinal blood vessels deviated largely for the CLRIS and VDIS datasets. The success rate of BIB010 on HRIS dataset was degraded due to the presence of diabetic abnormalities nearby the vessel boundaries. As the images of VDIS dataset are noisy with lower resolution which is mostly used in pathological purpose, the testing of several algorithms on this dataset led to large deviation and poor accuracy result BIB010 . The edges of the vessels of CLRIS dataset are highly blurred which can also affect the performance of algorithm BIB005 .