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Retinal Blood Vessel Tortuosity Measurement for Analysis of Hypertensive Retinopathy
Abstract
The retinal blood vessels act as landmark for detecting some retinal diseases. The changes in the vessel
characteristics such as tortuosity may be used to estimate the effect of hypertension. In this paper, a simple distance metric (DM) method is used to evaluate the tortuosity of blood vessels. Initially, the method is applied on synthetically generated blood vessels of normal, moderate and severely tortuous nature. Then retinal vessels are segmented using image processing methods. Small vessel segments are selected manually and used for tortuosity evaluation. These images are collected from local eye hospital, DRIVE database and VICAVR database. The results obtained show that the method is capable enough to estimate the tortuosity and have close correlation with subjective evaluation of tortuosity.
... In [22], the authors proposed a method for evaluating the tortuosity of retinal blood vessels by using the method of distance metric. The steps followed for tortuosity measurement include pre-processing and blood vessel detection. ...
... The literature survey reveals that limited works are done for tortuosity measurement of retinal blood vessels using different interpolation techniques like quadratic interpolation [14], Bezier and spline interpolation [17]. Some of the drawbacks of the discussed methods for tortuosity measurement are limited dataset [14,15,18], absence of subjective evaluation process [14][15][16], publicly unavailable databases [15,17,18,23], use of complex software for tortuosity evaluation [17,18,23], results not tested on standard tortuous database RET-TORT [15][16][17][18]22,23]. Here, we propose a method to measure the tortuosity of retinal blood vessels quantitatively using polynomial modeling of retinal blood vessel segments. ...
Tortuosity is one of the micro vascular change that is observed in many retinopathies. Its early detection can prevent the progression of various retinopathies to a critical stage at which a person may become blind. Here, we propose a novel method for the measurement of tortuosity by polynomial modeling of retinal vessels for the analysis of hypertensive retinopathy. The proposed method is tested on a set of 30 arteries and 30 veins vessel images collected from the Retinal Vessel Tortuosity Dataset (RET-TORT). Also, 90 vessel segments from Digital Retinal Images for Vessel Extraction (DRIVE) and 149 vessel segments from High Resolution Fundus (HRF) databases are used for tortuosity evaluation. The experimental results demonstrate that the order of the polynomial increases with the increase in the tortuosity of the blood vessels. Hence, the order of the polynomial can be used as a parameter to classify vessels as non-tortuous and tortuous. The results of the method are also evaluated subjectively and the inter-rater agreement analysis is made by using Fleiss Kappa index. The Spearman's rank order correlation coefficient is used to analyze the correlation between the ranking provided by the expert in the RET-TORT database and the ranking obtained by the proposed method. The results demonstrate that this method is capable of evaluating the tortuosity and classify vessel segments into non-tortuous or tortuous effectively. © 2019 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
... Wiharto et al. [40] extracted features using the fractal dimension and the lacunarity and classify the HR by implementing the ensemble random forest and achieved the accuracy of 88%, sensitivity of 91.3% and specificity of 85.1% on STARE dataset. Chetia et al. [32] evaluated the tortuosity of the blood vessels utilizing the distance metric technique to detect the HR on the VICAVR and DRIVE datasets. ...
Hypertension retinopathy is a retinal disease caused due to hypertension which leads to vision loss and blindness. Ophthalmologists use clinical methods to perform the diagnosis, which takes more time and money. Still, the computer-aided diagnostic system detects and grades Hypertensive Retinopathy with no time and is less expensive. This paper introduces an automated system that identifies hypertension retinopathy in the early stage of hypertension. Retinal image segmentation efficiently detects eye ailments, which are the signs of major eye diseases caused by hypertension, diabetes, and age-related macular disorders. This study uses fuzzy logic techniques in digital image processing and mainly concentrates on the early detection of hypertension retinopathy by using a nature-inspired optimization algorithm. Improved Fuzzy C-Means clustering identifies the lesion regions in hypertensive retinopathy accurately. The present model is tested on the publicly available online dataset, and its outcomes are compared with distinguished published methods. This study calculates the segmented output on the optimized features using the improved loss function in the Resnet-152 model. The proposed approach improves performance and surpasses the existing state-of-the-art models.
... These databases contain a large number of retinal images, thus allowing us to test the system to its full potential. 22 For each of these images, five horizontal, five vertical, and five diagonal blood vessel segments were considered. This implies that, from a single retinal image, 15 blood vessel (artery and vein) segments were considered. ...
Purpose: The study of retinal blood vessel morphology is of great importance in retinal image analysis. The retinal blood vessels have a number of distinct features such as width, diameter, tortuosity, etc. In this paper, a method is proposed to measure the tortuosity of retinal blood vessels obtained from retinal fundus images. Tortuosity is a situation in which blood vessels become tortuous, that is, curved or non-smooth. It is one of the earliest changes that occur in blood vessels in some retinal diseases. Hence, its detection at an early stage can prevent the progression of retinal diseases such as diabetic retinopathy, hypertensive retinopathy, retinopathy of prematurity, etc. The present study focuses on the measurement of retinal blood vessel tortuosity for the analysis of hypertensive retinopathy. Hypertensive retinopathy is a condition in which the retinal vessels undergo changes and become tortuous due to long term high blood pressure. Early recognition of hypertensive retinopathy signs remains an important step in determining the target-organ damage and risk assessment of hypertensive patients. Hence, this paper attempts to estimate tortuosity using image-processing techniques that have been tested on artery and vein segments of retinal images. Design: Image processing-based model designed to measure blood vessel tortuosity. Methods: In this paper, a novel image processing-based model is proposed for tortuosity measurement. This parameter will be helpful for analyzing hypertensive retinopathy. To test the eff ectiveness of the system in determining tortuosity, the method is first applied on a set of synthetically generated blood vessels. Then, the method is repeated on blood vessel (both artery and vein) segments extracted from retinal images collected from publicly available databases and on images collected from a local eye hospital. The blood vessel segment images that are used in the method are binary images where blood vessels are represented by white pixels (foreground), while black pixels represent the background. Vessels are then classified into normal, moderately tortuous, and severely tortuous by following the analysis performed on the images in the Retinal Vessel Tortuosity Data Set (RET-TORT) obtained from BioIm Lab, Laboratory of Biomedical Imaging (Padova, Italy). This database consists of 30 artery segments and 30 vein segments, which were manually ordered on the basis of increasing tortuosity by Dr. S. Piermarocchi, a retinal specialist belonging to the Department of Ophthalmology of the University of Padova (Italy). The artery and vein segments with the fewest number of turns are given a low tortuosity ranking, while those with the greatest number of turns are given a high tortuosity ranking by the expert. Based on this concept, our proposed method defines retinal image segments as normal when they present the fewest number of twists/turns, moderately tortuous when they present more twists/turns than normal but fewer than severely tortuous vessels, and severely tortuous when they present a greater number of twists/turns than moderately tortuous vessels. On implementing our image processing-based method on binary blood vessel segment images that are represented by white pixels, it is found that the vessel pixel (white pixels) count increases with increasing vessel tortuosity. That is, for normal blood vessels, the white pixel count is less compared to moderately tortuous and severely tortuous vessels. It should be noted that the images obtained from the different databases and from the local hospital for this experiment are not hypertensive retinopathy images. Instead, they are mostly normal eye images and very few of them show tortuous blood vessels. Results: The results of the synthetically generated vessel segment images from the baseline for the evaluation of retinal blood vessel tortuosity. The proposed method is then applied on the retinal vessel segments that are obtained from the DRIVE and HRF databases, respectively. Finally, to evaluate the capability of the proposed method in determining the tortuosity level of the blood vessels, the method is tested with a standard tortuous database, namely, the RET-TORT database. The results are then compared with the tortuosity level mentioned in the database. It was found that our method is able to classify blood vessel images as normal, moderately tortuous, and severely tortuous, with results closely matching the clinical ordering provided by the expert in the RET-TORT database. Subjective evaluation was also performed by research scholars and postgraduate students to cross-validate the results. Conclusion: The close correlation between the tortuosity evaluation using the proposed method and the clinical ordering of retinal vessels as provided by the retinal specialist in the RET-TORT database shows that, although simple, this method can evaluate the tortuosity of vessel segments effectively.
... Image retina is an image with three channels, namely red, green and blue, so that the conversion process to grayscale is needed. This components have different qualities, where the best quality is the green channel [1]. This makes a lot of research using green channels, except for a number of studies that do diagnosis referring to the retinal texture image. ...
Hypertensive retinopathy is a disease caused by acute high blood pressure. Examination of the disease can be done by analyzing the retina of the eye. Analysis can be done automatically by using the image processing of the retina from fundus cameras. The automation model is widely developed by combining a number of segmentation methods, classification algorithms and feature extraction. Unfortunately no one has reviewed a number of existing studies on the diagnosis of hypertensive retinopathy. In this study aims to conduct a review of a number of studies in the period 2010-2018 on the diagnosis of hypertensive retinopathy. The focus of the review is on the method of segmentation, feature extraction, and the classification algorithm used in the diagnostic model of hypertensive retinopathy. The review also includes comparisons of a number of diagnostic models that have been developed, and suggestions for further model development.
... The conversion of these values will separate the vessel paths. Each of these sections may contain one or more than one vessels [12] [13]. Each of the vessels in such sections is considered in any order. ...
This paper establishes an efficient color space for the contrast enhancement of myocardial perfusion images. The effects of histogram equalization and contrast limited adaptive histogram equalization are investigated and the one which gives good enhancement results is extended to the suitable color space. The color space which gives better results is chosen experimentally. Uniqueness of this work is that contrast limited adaptive histogram equalization technique is applied to the chrominance channels of the cardiac nuclear image, leaving the luminance channel unaffected which results in an enhanced image output in color space.
Edge detection is an important aspect in image processing. When a noisy image is presented for edge detection, the noise creates problem in the process of edge detection using conventional methods. One of the disadvantages of the conventional methods is that the noise is not removed automatically. The present paper proposes a novel approach for noise removal cum edge detection for both gray scale and binary images using morphological operations. Two images consisting of noise are processed and the effectiveness of the proposed approach is experimentally demonstrated. The results demonstrate that the proposed filter cum edge detector approach overcomes the deficiency of conventional methods and efficiently removes the noise and detects the edges.
This paper describes a novel tortuosity measure, based on the premise that tortuosity is a measure of deviation from an ideal non-tortuous vessel. Hence, we propose to model the overall shape of an ideal vessel as a quadratic polynomial at a larger scale while the deviations are modeled as quadratic polynomials at smaller scales. Thus, a given vessel center-line is decomposed as a sum of quadratic polynomials of decreasing scale. This Quadratic Polynomial Decomposition is used as a framework for defining a quantitative measure of tortuosity. As opposed to the existing proposed measures, our method can distinguish between the relative size, shapes and orientations of the vessel bends. The measure is position and scale invariant and satisfies two key desired properties: it varies directly with frequency of twists at fixed amplitude and it varies directly with amplitude of twists when their frequency is fixed. The proposed method has been tested on a standard data set containing 30 artery and 30 vein vessel segments, and shown to be among the best measures as compared to the results of existing methods.
Image binarization is the process of separation of pixel values into two
groups, white as background and black as foreground. Thresholding plays
a major in binarization of images. Thresholding can be categorized into
global thresholding and local thresholding. In images with uniform
contrast distribution of background and foreground like document images,
global thresholding is more appropriate. In degraded document images,
where considerable background noise or variation in contrast and
illumination exists, there exists many pixels that cannot be easily
classified as foreground or background. In such cases, binarization with
local thresholding is more appropriate. This paper describes a locally
adaptive thresholding technique that removes background by using local
mean and mean deviation. Normally the local mean computational time
depends on the window size. Our technique uses integral sum image as a
prior processing to calculate local mean. It does not involve
calculations of standard deviations as in other local adaptive
techniques. This along with the fact that calculations of mean is
independent of window size speed up the process as compared to other
local thresholding techniques.
The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a Mahalanobis distance metric for kNN classification from labeled examples. The Mahalanobis metric can equivalently be viewed as a global linear transformation of the input space that precedes kNN classification using Euclidean distances. In our approach, the metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. As in support vector machines (SVMs), the margin criterion leads to a convex optimization based on the hinge loss. Unlike learning in SVMs, however, our approach requires no modification or extension for problems in multiway (as opposed to binary) classification. In our framework, the Mahalanobis distance metric is obtained as the solution to a semidefinite program. On several data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification. Sometimes these results can be further improved by clustering the training examples and learning an individual metric within each cluster. We show how to learn and combine these local metrics in a globally integrated manner.
We propose a novel index for quantitating arterial tortuosity, based on second differences of the coordinates of the vessel midline. The index is independent of the magnification of the image and of sampling frequency. We have demonstrated its validity as an indicator of changes in morphology using simulated shapes. It is superior to two other putative indices based on vessel elongation and the variance of differences in the vessel midline coordinates, presented previously in the literature. The proposed index is easily generalised to three dimensions, allowing a true three-dimensional tortuosity index to be calculated from biplane angiograms or serial tomographic slices. It has a number of clinical applications. Preliminary data, using 82 aortograms from radiographic archives, showed a significant correlation between the tortuosity of the abdominal aorta and subject age (r=0.684, P<0.001). For this group, increased tortuosity was especially evident for subjects above 40 yr of age.
Tortuosity is among the first alterations in the retinal vessel network to appear in many retinopathies, such as those due to hypertension. An automatic evaluation of retinal vessel tortuosity would help the early detection of such retinopathies. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. This justifies the need for a new definition, able to express in mathematical terms the tortuosity as perceived by ophthalmologists. We propose here a new algorithm for the evaluation of tortuosity in vessels recognized in digital fundus images. It is based on partitioning each vessel in segments of constant-sign curvature and then combining together each evaluation of such segments and their number. The algorithm has been compared with other available tortuosity measures on a set of 30 arteries and one of 30 veins from 60 different images. These vessels had been preliminarily ordered by a retina specialist by increasing perceived tortuosity. The proposed algorithm proved to be the best one in matching the clinically perceived vessel tortuosity.
A fully automated system for retinal vessel tortuosity system diagnosis is proposed in this paper. Our diagnosis system includes: (1) automated retinal vessel segmentation and tracing; (2) computerized tortuosity grading. In recent years, many works have been done on computerized diagnosis of retinal vessel tortuosity. But there are few researchers working on a fully automated system. The major difficulties in producing a fully automated system includes: (1) automated tracing of vessels to identify each individual branch; (2) global tortuosity grading of a retinal vessel image. In this paper, we propose a scheme to trace and grade retinal vessels using scale (variant widths and lengths of vessel segments) dependent techniques. The experimental results show that our system is useful in clinical applications.
From the Publisher:True computer imaging for engineers! Digital signal processing has long been the domain of electrical engineers, while the manipulation of image data has been handled by computer scientists. The convergence of these two specialties in the field of Computer Vision and Image Processing (CVIP) is the subject of this pragmatic book, written from an applications perspective and accompanied by its own educational and developmentsoftware environment, CVIPtools. Illustrated with hundreds of examples, Computer Vision and Image Processing brings together the theory of computer imaging with the tools needed for practical research and development. The first part of Computer Vision and Image Processing presents a system model for each of the major application areas of CVIP, relating each specific algorithm to the overall process of applications development. The areas covered are: Image analysis Image restoration Image enhancement Image compression Computer Vision and Image Processing's second half focuses on the use of the CVIPtools environment, the software developed especially by the author and included on the accompanying CD-ROM. These advanced chapters discuss: Software features and applications CVIPtools software development environment Library descriptions and function prototypes CVIPtools is a GUI-based application, which includes an extended Tcl shell, that is ANSI-C compatible and runs on most flavors of UNIX and Windows NT/95. To get the most out of Computer Vision and Image Processing, a basic background in mathematics and computers is necessary. Knowledge of the C programming language will enhance the usefulness of the algorithms used in programming, and an understanding of signal and system theory is helpful in mastering transforms and compression. Engineers, programmers, graphics specialists, multimedia developers, and medical imaging professionals will all appreciate Computer Vision and Image Processing's solid introduction for anyone who uses computer imaging.
Tortuosity is one of parameters which describe a state of the eye fundus blood vessels. Tortuosity can be estimated from the detected vessels in optical fundus images. The increase in vessel tortuosity was observed in eyes of patients with advanced background diabetic retinopathy, papilloedema, even in some completely healthy eyes (in this case tortuosity does not change in time). Though many methods to estimate eye vessel tortuosity exist, dependencies between tortuosity and parameters of cardiovascular system are not fully explored. In this paper we studied whether different tortuosity estimation algorithms can detect the change of blood pressure in the cylindrical segment of the vessel modeled using finite elements method. In addition we studied how does one inhomogenity added inside the blood vessel influence the tortuosity and what are the relationships between the different tortuosity estimates and blood pressure? We found that even single inhomogenity of the vessel wall triggers the increase of tortuosity when inner blood pressure increases. The resulting dependencies among different tortuosity estimates and blood pressure are mostly nonlinear.
Elements of Visual Perception
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