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International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 VESSEL SEGMENTATION GRAPH FOR IDENTIFY THE RETINAL IMAGE FOR CARDIOVASCULAR DISEASES BHUVANESWARI.S1 Department of ECE, MeenakshiRamaswamy Engineering College, Tamil Nadu India, SATHIYA PRIYA.T2 Assistant professor,Department of ECE [email protected] T.Thirumurugan3 Head of the Department,department of ECE MeenakshiRamaswamy Engineering College Tamil Nadu, India. . ABSTRACT A Retinal image provides a good diagnostic approach of what is happening inside a human body. In this project, we automatically extract the optic disc in retinal images by pixel based segmentation and optic cup by medial axis detection and ellipse fitting method. In existing method the optic cup and optic disc are segmented by creating mask manually and from that the glaucoma is detected. But its experimental result doesn’t close to clinical CDR value. But in our proposed system, we don’t need to select the OD and OC boundary by creating mask. The OD is the high intensity part of an eye. So we easily extract the disc boundary by medial axis detection used in pixel based segmentation. Cup segmentation is much more challenging compared to disc segmentation due to the presence of high density vascular architecture in the region of the optic cup traversing the cup boundary and this project; and we address the problem of automatically identifying true vessels as a post processing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the © 2016, IJSRETS All Rights Reserved problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. Then our experimental results show that successfully diagnosis the glaucoma and cardio vascular diseases. Key words: CRVE, CRAE, IPACHI, MIPAV, API, MFMK, OCT, CNV INTRODUCTION 1.1 RETINAL IMAGE Digital retinal imaging is a relatively new technology that can be used to assess patients for diabetic retinopathy. Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. An accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the patho-physiology of diabetes. A retinal image provides a snapshot of what is happening inside the human body. In particular, the state of the retinal vessels has been shown to reflect the cardiovascular condition of the body. Page | 6 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 Measurements to quantify retinal vascular structure and properties have shown to provide good diagnostic capabilities for the risk of cardiovascular diseases. Retinal image processing is an important tool in diagnosing and curing the diseases that influence the retina. Diagnosis and treatment of several disorders affecting the retina require capturing a sequence of fundus images using the fundus camera. These images are to be processed for better diagnosis and planning of treatment. Retinal image segmentation is greatly required to extract certain features that may help in diagnosis and treatment. Also registration of retinal images is very useful in extracting the motion parameters that help in composing a complete map for the retina as well as in retinal tracking. Blood vessels in ophthalmoscope images play an important role in diagnosis of some serious pathology on retinal images. Hence, accurate extraction of vessels is becoming a main topic of this research area. Retinal images obtained using Adaptive Optics have the potential to facilitate early detection of retinal pathologies. The retina is the only location where blood vessels can be directly visualized. Increasing technology leading to the development of digital imaging systems over the past two decades has revolutionized fundal imaging. Whilst digital imaging does not still have the resolution of conventional photography, modern digital imaging systems offer very high-resolution images that are sufficient for most clinical scenarios The retina is the only location where blood vessels can be directly visualized non-invasively in vivo. Increasing technology leading to the development of digital imaging systems © 2016, IJSRETS All Rights Reserved over the past two decades revolutionized fundal imaging. has Fig 1.1.1 Retinal image Thus, because of its architecture dictated by its function both diseases of the eye, as well as diseases that affect the circulation and the brain can manifest them in the retina. These include ocular diseases, such as macular degeneration and glaucoma, the first and third most important causes of blindness in the developed world. A number of systemic diseases also affect the retina. blindness in the developed world, hypertensive retinopathy from cardiovascular disease, and multiple sclerosis. Thus, on the one hand, the retina is vulnerable to organspecific and systemic diseases, while on the other hand, imaging the retina allows diseases of the eye proper, as well as complications of diabetes, hypertension and other cardiovascular diseases, to be detected, diagnosed and managed. 1.2 OPTIC DISC ANALYSIS Diabetic retinopathy, hypertension, glaucoma, and macular degeneration are Page | 7 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 nowadays some of the most common causes of visual impairment and blindness. Early diagnosis and appropriate referral for treatment of these diseases can prevent visual loss. Usually, more than 80% of global visual impairment is avoidable, and in the case of diabetes by up to 98%. All of these diseases can be detected through a direct and regular ophthalmologic examination of the risk population. However, population growth, aging, physical inactivity and rising levels of obesity are contributing factors to the increase of them, which causes the number of ophthalmologists needed for evaluation by direct examination is a limiting factor. So, a system for automatic recognition of the characteristic patterns of these pathological cases would provide a great benefit. Regarding this aspect, optic disc (OD) segmentation is a key process in many algorithms designed for the automatic extraction of anatomical ocular structures, the detection of retinal lesions, and the identification of other fundus features.First, the OD location helps to avoid false positives in the detection of exudates associated with diabetic retinopathy, since both of them are spots with similar intensity.Secondly, the OD margin can be used for establishing standard and concentric areas in which retinal vessel diameter measurements are performed by calculating some important diagnostic indexes for hypertensive retinopathy, such as central retinal artery equivalent (CRAE) and central vein equivalent. © 2016, IJSRETS All Rights Reserved Fig 1.2.1 Optic Disc and Cup The relation between the size of the OD and the cup (cup-disc-ratio) has been widely utilized for glaucoma diagnosis. In addition, the relatively constant distance between the OD and the fovea is useful for estimating the location of the macula, area of the retina related to fine vision. 1.3 BASIC CONCEPTS 1.3.1 BIOMEDICAL ENGINEERING Biomedical engineering (BME) is the application of engineering principles and design concepts to medicine and biology for healthcare purposes. This field seeks to close the gap between engineering and medicine: It combines the design and problem solving skills of engineering with medical and biological sciences to advance healthcare treatment, including diagnosis, monitoring, and therapy. Much of the work in biomedical engineering consists of research and development, spanning a broad array of subfields. Prominent biomedical engineering applications include the development of biocompatible prostheses, various diagnostic and therapeutic medical Page | 8 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 devices ranging from clinical equipment to micro-implants, common imaging equipment such as MRIs and EEGs, regenerative tissue growth, pharmaceutical drugs and therapeutic biological. Biomedical engineering can be viewed from two angles, from the medical applications side and from the engineering side. A biomedical engineer must have some view of both sides. As with many medical specialties some BME subdisciplines are identified by their associations with particular systems of the human body, such as: Cardiovascular technology - which includes all drugs, biologics, and devices related with diagnostics and therapeutics of cardiovascular systems Neural technology - which includes all drugs, biologics, and devices related with diagnostics and therapeutics of the brain and nervous systems Orthopaedic technology - which includes all drugs, biologics, and devices related with diagnostics and therapeutics of skeletal systems Cancer technology - which includes all drugs, biologics, and devices related with diagnostics and therapeutics of cancer 1.4 RETINAL OPHTHALMOLOGY Generally an image is a twodimensional function f(x,y).The amplitude of image at any point say f is called intensity of the image. It is also called the gray level of image at that point. We need to convert these x and y values to finite discrete values to form a digital image. The input image is a fundus taken from © 2016, IJSRETS All Rights Reserved stare data base and drive data base. The image of the retina is taken for processing and to check the condition of the person we need to convert the analog image to digital image to process it through digital computer. Each digital image composed of a finite elements and each finite element is called a pixel. We can also use filter to improve selectivity. We can generate a 2D image using single sensor with a displacement in both directions of plane. The arrangement used here is for high precision scanning where film negative is mounted on to a drum which produces mechanical rotation. This mechanical rotation provides displacement in one direction. A sensor mounted on a lead screw is used as it provides motion in perpendicular direction. Using his we can control mechanical motion effectively and images are obtained with high resolution. researchers were working on retinal images to perform various image processing tasks for the beneficial of health sector. Currently many researchers have proved the automatic assessment of quality of retinal images taken by fundal camera with a reference image. Recently, AO has been combined with scanning laser ophthalmoscope and optical coherence tomography (OCT) to obtain images of retinal microvasculature and blood flow and three dimensional images of living cone photoreceptors respectively. The photoreceptors are one of the key components of the retinal images, which act as an indicator to detect or monitor retinal diseases.Retinal vascular imaging is currently used in 2 broad areas of cardiovascular research. First, retinal imaging is a novel, noninvasive research tool to probe the role and pathophysiology of the microvasculature, typically defined Page | 9 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 as vessels between 100 and 300 μm in size, in the development of clinical cardiovascular disease. Second, retinal vascular imaging is explored in clinical settings as a risk stratification tool to aid clinicians in identifying patients with micro vascular signs who are at high risk of future clinical cardiovascular and cerebrovascular events. A third possibility is under investigation: retinal vascular imaging has potential as a surrogate measure of the micro vascular benefits of new therapeutic agents in early phase. 1.5 CARDIOVASCULAR DISEASE Cardiovascular disease (also called heart disease) is a class of diseases that involve the heart, the blood vessels (arteries, capillaries, and veins) or both. Cardiovascular disease refers to any disease that affects the cardiovascular system, principally cardiac disease, vascular diseases of the brain and kidney, and peripheral arterial disease. The causes of cardiovascular disease are diverse but atherosclerosis and/or hypertension are the most common. Additionally, with aging come a number of physiological and morphological changes that alter cardiovascular function and lead to subsequently increased risk of cardiovascular disease, even in healthy asymptomatic individuals. Traditional risk factors such as hypertension, hyperlipidemia, diabetes, etc. allow physicians to treat high risk patients, but a substantial proportion of cardiovascular disease is not explained by traditional risk factors alone. In the past two decades, an increased awareness of the contribution of coronary micro vascular disease to the overall heart disease burden © 2016, IJSRETS All Rights Reserved has heightened interest in using the retinal microvasculature as a marker for coronary disease. The unique perspective offered by retinal vascular image analysis is used by an increasing number of researchers and research groups to address scientific questions that are difficult to answer through other means. A key question in cardiovascular physiology is whether changes detected in the microvasculature are early markers of vascular disease secondary to the disease process or whether microvasculature changes represent the primary causes that contribute etiologically and mechanistically to the development of vascular disease. However, one of the central unresolved issues in understanding the patho physiology of hypertension is whether arteriolar narrowing is antecedent to and contributes to the development of hypertension, or whether it is consequential to and represents a secondary adaptation to elevated blood pressure, or whether both processes occur. Retinal vascular caliber (CRAE and CRVE) was analyzed as continuous variables. We used analysis of covariance to estimate mean retinal vascular caliber associated with the presence versus absence of categorical variables or increasing quartiles of continuous variables adjusted for age and sex. We constructed two multivariate models. First, in model 1, we constructed a model for CRAE initially including variables that were significantly associated with CRAE in age and sex analyses. Final variables were selected based on a stepwise backward approach, adjusting for age. We then constructed a similar model for CRVE based on variables significantly associated with CRVE in age and sex Page | 10 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 analyses. Second, in model 2, we preformed supplementary analysis by additional adjustment for the fellow retinal vessel caliber (i.e., CRVE was included as an independent variable in the model for CRAE, and vice versa), as previously described to control for potential confounding from fellow vessel diameter. We calculated sequential R2 to indicate the contribution of each independent variable to the model. Data presented here are based on results from model 1, as results from model 2 were largely similar. 1.6 GLAUCOMA Glaucoma is one of the common cause of blindness with about 79 million people in the world is likely to be affected by glaucoma by the year 2020. It is a complicated disease in which damage to the optic nerve leads to progressive, irreversible vision loss. The optic nerve head carries from 1 to 1.2 million neurons which carries visual information from eye towards the brain. The ratio of optic disc cup and neuroretinal rim. It is an important structural indicator for accessing the presence and progression of glaucoma. Various parameters are estimated and recorded to detect the glaucoma stage which include cup and OD diameter, area of OD and rim area etc. Efforts have been made to automatically detect glaucoma from 3-D images, but high cost makes them not appropriate for large scale screening. Colour fundus imaging (CFI) is one of the method that is used for glaucoma assessment. It has emerged as a preferred method for large-scale retinal disease screening and is established for screening of large-scale diabetic retinopathy. The work published on automated detection can be divided in three main strategies.without disk parameterization. In this a set of features are computed from CFI and two-class classification is employed to find a image as normal or glaucomatous. These features are computed without performing OD and cup segmentation.with disk parameterisation with monocular CFI, and with disk parameterization using stereo CFI. In these strategies which are based on disk parameterisation, OD and cup regions are segmented to estimate disk parameters. A stereo CFI gives better characterisation compared to monocular CFI. 2.1 SYSTEM DESCRIPTION © 2016, IJSRETS All Rights Reserved Page | 11 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 Preprocessing Image Acquisition Gray scale conversion Upload Retinal images Noise Removal Sharpen filter Preprocessed retinal image Vessel segmentation Features extraction Ellipse fitting method Graph theoretical approach Optic disc and cup segmentation Similar vessel pixels Vessel Segmentation Map Retinal diseases © 2016, IJSRETS All Rights Reserved Perimeter Calculation Vessel Classification SVM classification Artery-Vein Classified image Disease prediction CRAE and CRVE measurements Optic disk and cup ratio Page | 12 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 Fig 2.1.1 Block diagram inside a human body. By analyzing a retinal image one can identify cardio vascular condition of the body. The risk of cardio vascular diseases such as stroke, hypertension, etc.., can be 3.1 SYSTEM DESIGN Upload datasets found by detailed quantification of a Preprocessing retinal vascular structure and its properties. Optic disc and Cup Segmentation In existing system the identification of true Vessel Tracking vessel becomes difficult due to Performance Evaluation Bifurcations and Crossovers. The identification of wrong vessel will lead to wrong clinical diagnose. 4.1 MODULES DESCRIPTION 4.1.1 UPLOAD DATASETS In this module, we upload the 4.1.2 PREPROCESSING retinal images. The fundus of the eye is the In this module, we perform the interior surface of the eye, opposite gray scale conversion operation to identify the lens, and includes the retina, optic black and white illumination and to disc, macula and fovea, and posterior pole. perform the filtering operations using the The fundus can be examined Median filter is to filter out noise that has by ophthalmoscopyand/or fundus corrupted image. It is based on a statistical photography.The retina is a layered approach. Typical filters are designed for a structure with several layers desired frequency response. Median of neurons interconnected by synapses. In filtering is a nonlinear operation often used retina we can identify the vessels. in image processing to reduce "salt and The retina is a layered tissue lining pepper" noise. A median filter is more the interior of the eye that enables the effective than convolution when the goal is conversion of incoming light into a neural to simultaneously reduce noise and signal that is suitable for further preserve edges. Color fundus images often processing in the visual cortex of the brain. show important lighting variations, poor It is thus an extension of the brain. contrast and noise. In order to reduce these The ability to image the retina and imperfections and generate images more develop techniques for analyzing the suitable for extracting the pixel features images is of great interest. As its function demanded in the classification step. requires the retina to see the outside world, the involved ocular structures have to be 4.1.3 OPTIC DISC AND CUP optically transparent for image formation. SEGMENTATION Thus, with proper techniques, the retina is This module we propose a novel visible from the outside, making the retinal OD segmentation method based on regiontissue, and thereby brain tissue, accessible based active contour model to improve the for imaging noninvasively. segmentation on the range of OD A Retinal image provides a good instances. The scope of this model is diagnostic approach of what is happening enhanced by including image information © 2016, IJSRETS All Rights Reserved Page | 13 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 at a support domain around each point of interest. The first step is to localize the OD region and extract a region of interest for further processing. Next, the edges near the identified center location in the image domain are used to estimate the radius of the circle. The circle points are identified using estimated radius and used to initialize the active contour.The detection of OD manually by experts is a standard procedure for this. There have been efforts for OD segmentation but very few methods for the cup segmentation. Finding the cup region helps in finding the cup-todisk (CDR) which is also an important property for identifying the disease. In this project, we present an automatic cup region segmentation method based on medial axis method. Cup segmentation: In ellipse fitting method, the green channel of the extracted optic disc was processed using histogram analysis to determine a threshold value, which segments out the pixels corresponding to the top 1/3 of the grayscale intensity, was used to define the initial contour in the ROI. In order to fit ellipses specifically while retaining the efficiency of solution of the linear leastsquares problem. repeatedly finding the next vessel point with a scoring function that considers the pixel intensity and orientation in the vicinity of the current point in the image. Bifurcations and crossovers are detected using some intensity profile. Tracking for the same vessel then continues along the most likely path. This approach of tracking vessels one-at-a-time does not provide sufficient information for disambiguating vessels at bifurcations and crossovers.We can perform the vessel segmentation using the fuzzy segmentation algorithm. Fuzzy segmentation is an effective way of segmenting out objects in pictures containing both random noise and shading. This is illustrated both on mathematically created pictures and on some obtained from medical imaging. We segment vessels in retina with several widths. Vessels are extracted from retinal images. In this module we apply the method aims to identify vessels from vessel segmentation and represent them in the form of binary trees for subsequent vessel measurements. It has two main steps: 1) identify crossovers, and 2) search for the optimal forest (set of vessel trees).A crossover segment occurs when two different vessels share a segment. 4.1.4 VESSEL TRACKING Retinal vessel extraction involves segmentation of vascular structure and identification of distinct vessels by linking up segments in the vascular structure to give complete vessels. One branch of works, termed vessel tracking, performs vessel segmentation and identification at the same time. These methods require the start points of vessels to be predetermined. Each vessel is tracked individually by 4.1.5 PERFORMANCE EVALUATION In this module we evaluate the performance using graph tracer algorithm by using the measurements using the precision and recall measurement. Our graph tracer algorithm better performance than the existing approaches. We conclude that tracing all vessels simultaneously is better than tracing vessels individually without current knowledge of other vessels. Then our algorithm extracts the © 2016, IJSRETS All Rights Reserved Page | 14 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 true blood vessels simultaneously other than one vessel per time. The Normal cup to disc ratio range is from 0.1 to 0.3. If the cup to disc ratio exceeds 0.3 then it indicates the abnormal condition that is the presence of glaucoma. 4.1.6 RETINAL IMAGE ACQUISITION Retinal images of humans play an important role in the detection and diagnosis of cardio vascular diseases that including stroke, diabetes, arteriosclerosis, cardiovascular diseases and hypertension, to name only the most obvious. Vascular diseases are often life-critical for individuals, and present a challenging public health problem for society. Therefore, the detection for retinal images is necessary, and among them the detection of blood vessels is most important. Fig 4.1.6.1 Retinal Image Acquisition The alterations about blood vessels, such as length, width and branching pattern, can not only provide information on pathological changes but can also help to grade diseases severity or automatically © 2016, IJSRETS All Rights Reserved diagnose the diseases. In this module, we upload the retinal images. The fundus of the eye is the interior surface of the eye, opposite the lens, and includes the retina, optic disc, macula and fovea, and posterior pole. The fundus can be examined by ophthalmoscopyand/or fundus photography. The retina is a layered structure with several layers of neurons interconnected by synapses. In retina we can identify the vessels. Blood vessels show abnormalities at early stages also blood vessel alterations. Generalized arteriolar and venular narrowing which is related to the higher blood pressure levels, which is generally expressed by the Arteriolar-to-Venular diameter ratio. In this work, we have constructed a dataset of images for the training and evaluation of our proposed method. This image dataset was acquired from publically available datasets such as DRIVE and STAR. Each image was captured using 24 bit per pixel at 760 x 570 pixels. First, proposed method has only been tested against normal images which are easier to distinguish. Second, some level of success with abnormal vessel appearances must be established to recommend clinical usage. As can be seen, a normal image consists of blood vessels, optic disc, fovea and the background, but the abnormal image also has multiple artifacts of distinct shapes and colors caused by different diseases. (a) normal image and (b) diseased image. The nodes are extracted from the centerline image by finding the bifurcation points which are detected by considering pixels with more than two neighbors and the endpoints or terminal points by pixels having just one Page | 15 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 neighbor. In order to find the links between nodes all the bifurcation points and their neighbors are removed from the centerline image and as result we get an image with separate components which are the vessel segments. On the other hand, any given link can only connect two. Vessels segmentation binary mask is created by detecting vessels edges from sharpened image. The blood vessels are marked by the masking procedure which assigns one to all those pixels which belong to blood vessels and zero to non vessels pixels. Final refined vessel segmentation mask is created by active contour model. Active contour model, also called snakes, is a framework in computer vision for delineating an object outline from a possibly noisy 2D image. A snake is an energy minimizing, deformable spline influenced by constraint and image forces that pull it towards object contours and internal forces that resist deformation. Snakes may be understood as a special case of the general technique of matching a deformable model to an image by means of energy minimization. In two dimensions, the active shape model represents a discrete version of this approach, taking advantage of the point distribution model to restrict the shape range to an explicit domain learned from a training set. Finally we provide the segmentation mask for preprocessed retinal images. DISCUSSIONS: SYSTEM ANALYSIS 5.1 EXISTING SYSTEM The existing algorithm is implemented in MATLAB environment. © 2016, IJSRETS All Rights Reserved The existing system was constructed and tested using DRIVE datasets. IPACHI segmentation is applied in order to extract features that contain vessel width. SVM classifiers to improve the accuracy of the retinal classification. . Examination of blood vessels in the eye allows detection of eye diseases such as glaucoma and diabetic retinopathy. Traditionally, the vascular network is mapped by hand in a timeconsuming process that requires both training and skill. Automating the process allows consistency, and most importantly, frees up the time that a skilled technician or doctor would normally use for manual screening. So we can implement automatic process to examine the blood vessels to identify the cardio vascular diseases in retinal images. The existing method utilizes the concept of active contours to remove noise, enhance the image, track the edges of the vessels, calculate the perimeter of vessels and identify the cardio diseases. Implement infinite perimeter active contour with hybrid region information (IPACHI) model to segment blood vessels and calculate perimeter of the blood vessels. Page | 16 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 5.1.1 DISADVANTAGES The existing method cannot provide segmentation accuracy in retinal and vessel segmentation. Wrong identification in bifurcation and crossover points. Existing system only follow the preprocessing steps but difficult to identify the vessels with noises in images. Track one vessel at time and also segment the one route per time. Only analyze the risk in cardiovascular diseases and not to identify the heart diseases. 5.2 PROPOSED SYSTEM In proposed system we implement the sharpen filtering technique to remove noise. This process is known as preprocessing. The objective is to segment the cup region by using both vessel bends and pallor information. Only a subset of these points defines the cup boundary. We refer to this as relevant vessel bends or rbends. A second problem is that the anatomy of the OD region is such that the r-bends are non-uniformly distributed across a cup boundary with more points on the top and bottom they are mostly absent in the nasal side and very few in number in the temporal side. We propose a local interpolating spline to naturally approximate the cup boundary in regions where rbends are absent. The OD region has both thick and thin vessels. Detecting both © 2016, IJSRETS All Rights Reserved reliably is difficult in the presence of inter-image intensity variations. During the tracking process, vessel edge points are detected iteratively using local grey level statistics and vessel's continuity properties. A method for automatic segmentation of blood vessels in retinal images. The method is based on vessel tracking technique. The fit the crossover profile, the split is treated as a crossover; otherwise, it is a bifurcation and the tracer will follow both paths. It is greedy because unless a crossover is identified, it will add all the connected pixels to the same vessel. 5.2.1 ADVANTAGES Efficient post processing step for tracking cross over points. Simultaneously identifying the blood vessels. Advanced approach for vessel structure segmentation. Easily identify the diseases with improved accuracy rate. . ABBREVIATIONS CRAE Central Retinal Artery Equivalent CRVE Central Retinal Vein Equivalent MIPAV Medical Image Processing Analysis And Visualization IPACHI Infinite Perimeter Active Contour With Hybrid Region Information Page | 17 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 SVM Support Vector Machine API CNV OCT Application Program Interface Choroidal Neo Vascularization Optical Coherence Tomography MF Matched Filter FDOG First-Order-Derivative Of The Gaussian Insight Segmentation And Registration Toolkit ITK MFMK OD FABC Matched Filters With wavelet Kernels Optic Disc Feature-Based Ad boost Classifier measurements. And we implement the post processing step to vessel segmentation. This step is used to track all true vessels and find the optimal forest. We can overcome wrong diagnosis of crossovers by using simultaneous identification of blood vessels from retina. The final goal of the proposed method is to make easier the early detection of diseases related to the blood vessels of retina. Multi RESULTS AND CONCULSION We conclude that, our proposed system implemented successfully with accurate identification of true vessels to obtain correct retinal ophthalmology measurements. This project presented a solution for glaucoma assessment which allows derivation of various geometric parameters of the OD. This is in contrast to earlier approaches which have largely focused on the estimation of CDR which varies considerably within normals. It is also well recognized that there is significant intra and inter observer error in manual assessment with this parameter. The presented work enables more comprehensive evaluation of the OD and performing glaucoma detection using multiple disk parameters. And our proposed system implemented successfully with accurate identification of true vessels to obtain correct retinal ophthalmology © 2016, IJSRETS All Rights Reserved REFERENCE [1] H. A. Quigley and A. T. Broman, “The number of people with glaucoma worldwide in 2010 and 2020,” Br. J. Ophthalmol., vol. 90, no. 3, pp. 262–267, 2006. [2] S. Y. Shen, T.Y.Wong, P. J. Foster, J. L. Loo,M.Rosman, S.C. Loon, W. L. Wong, S. M. Saw, and T. Aung, “The prevalence and types of glaucoma in malay people: The singaporemalay eye study,” Invest.Ophthalmol. Vis. Sci., vol. 49, no. 9, pp. 3846–3851, 2008. [3] P. J. Foster, F. T. Oen, D. Machin, T. P. Ng, J. G. Devereux, G. J. Johnson, P. T. Khaw, and S. K. Seah, “The prevalence of glaucoma in Chinese residents of Singapore: A cross-sectional population survey of the TanjongPagar district,” Arch. Ophthalmol., vol. 118, no. 8, pp. 1105– 1111, 2000. [4] Centre Eye Res. Australia, Tunnel vision: The economic impact of primary Page | 18 International Journal on Scientific Research in Emerging TechnologieS, Vol. 2 Issue 5, August 2016 ISSN: 1934-2215 open angle glaucoma 2008 [Online]. Available: http://nla.gov.au/ nla.arc-86954 [5] J. Meier, R. Bock,G.Michelson, L. G. Nyl, and J. Hornegger, “Effects of preprocessing eye fundus images on appearance based glaucoma classification,” in Proc. 12th Int. Conf. Comput. Anal. Images Patterns, 2007, pp. 165–172. [6] H. Li, W. Hsu, M. L. Lee, and T. Y. Wong, “Automatic grading of retinal vessel caliber,” IEEE Trans. Biomed. Eng., vol. 52, no. 7, pp. 1352–1355, Jul. 2005. [7] E. Grisan, A. Pesce, A. Giani, M. Foracchia, and A. Ruggeri, “A new tracking system for the robust extraction of retinal vessel structure,” in Proc. IEEE Eng. Med. Biol. Soc., Sep. 2004, vol. 1, pp. 1620–1623. [8] Y.Yin,M.Adel, M. Guillaume, and S. Bourennane, “Aprobabilistic based method for tracking vessels in retinal images,” in Proc. IEEE Int. Conf.Image Process., Sep. 2010, pp. 4081–4084. [9] M. Martinez-Perez, A. Highes, A. Stanton, S. Thorn, N. Chapman, A. Bharath, and K. Parker, “Retinal vascular tree morphology: A semiautomatic quantification,” IEEE Trans. Biomed. Eng., vol. 49, no. 8, pp. 912–917, Aug. 2002. [10] H. Azegrouz and E. Trucco, “Maxmin central vein detection in retinal fundus images,” in Proc. IEEE Int. Conf. Image Process., Oct. 2006, pp. 1925–1928. © 2016, IJSRETS All Rights Reserved Page | 19