GLAUCOMA is a chronic eye disease that can damage optic nerve. According to WHO It
is the second leading cause of blindness, and is predicted to affect around 80 million people by 2020.
Development of the disease leads to loss of vision, which occurs increasingly over a long period of
time. As the symptoms only occur when the disease is quite advanced so that glaucoma is called the
silent thief of sight. Glaucoma cannot be cured, but its development can be slowed down by
treatment. Therefore, detecting glaucoma in time is critical. However, many glaucoma patients are
unaware of the disease until it has reached its advanced stage. In this paper, some manual and
automatic methods are discussed to detect glaucoma. Manual analysis of the eye is time consuming
and the accuracy of the parameter measurements also varies with different clinicians. To overcome
these problems with manual analysis, the objective of this survey is to introduce a method to
automatically analyze the ultrasound images of the eye. Automatic analysis of this disease is much
more effective than manual analysis.
Glaucoma is the most leading cause of irreversible blindness with the population of Africa and Asia ranking the highest over the rate of glaucoma affected regions around the world. The defect will damage eyes irreversibly by affecting the optic cup and optic disc of an eye. The early detection of glaucoma is an unavoidable need in the medical field. The widely used technique to detect glaucoma is an invasive method that may lead to other effects on the eye. This reason led to the introduction of a non invasive method that follows image processing for the detection of glaucoma. Retinal image based detection is the best way to choose as it comes under non invasive methods of detection. Detection of glaucoma using retinal images requires various medical features of the eyes such as optic cup diameter, optic disc diameter and optic cup to disc ratio are used. Glaucoma disease detection from retinal images supports convolutional neural networks CNN . The textual features obtained from retinal images such as the optic cup to optic disc measures are used for this classification. Convolutional Neural Networks use little pre processing techniques that can be implemented relatively uncomplicated compared to other image classification techniques. The implementation of this project follows the traditional CNN architecture, applying filter layers such as Convolution layer and Pooling layer and also activation functions such as ReLu function and sigmoid function to pre process as well as to update weights respectively on the hidden layers of the CNN followed by classifying the image. Vishnubhotla Poornasree | Vijayagiri Ashritha | Venumula Deeksha Reddy | J. Srilatha ""Glaucoma Detection from Retinal Images"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23732.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23732/glaucoma-detection-from-retinal-images/vishnubhotla-poornasree
Glaucoma Detection in Retinal Images Using Image Processing Techniques: A SurveyEswar Publications
Glaucoma is a disease associated with human eyes and second conducting movement o fblindness across the globe if
eyes are not treated at preliminary stage. Glaucoma normally occurs with increased intra-ocular pressure (IOP) in eyes and gradually damagesthe vision field of eyes. The term ocular-hypertension is related to those people in whom IOP increases consistently and does not damage the optic nerve. Glaucoma has different types such as open-angle, close-angle, congenital, normal tension and etcetera. Normal tension glaucoma affects vision field and damages optic nerve as well. The term angle means the distance between iris and cornea; if this distance is large it is referred to as open-angle glaucoma and similarly if the distance between iris and cornea is short than this is called close-angle glaucoma. Open-angle glaucoma is common as compared to close-angle glaucoma. Close-angle glaucoma is very painful and affects vision field of eyes quickly as compared to open-angle glaucoma. In this
paper, the state of the art CAD systems and image processing methods are studied and compared systematically in terms of their classification accuracy, methodology approach, sensitivity and specificity. The comparison results indicate that the accuracy of these CAD systems and image processing methods is not up to the mark.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Detection of Glaucoma using Optic Disk and Incremental Cup Segmentation from ...theijes
Medical researchers, detection of eye disease is very important because it may causes blindness. Glaucoma is one of the diseases that cause blindness. Standard procedure for detection glaucoma is to analysis of optic disk (OD) and cup region in retinal image. In this paper, introduce an automatic OD parameterized technique which is based on segmentation and Incremental Cup segmentation. The incremental cup segmentation method is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is applied to derive a reliable subset of vessel bends called r-bends followed by a local 2-D spline fitting to derive the desired cup boundary. The results are compared with existing methods using different retinal images.
Development of novel BMIP algorithms for human eyes affected with glaucoma an...Premier Publishers
Glaucoma is one of the second driving eye maladies on the planet, if not treated legitimately may prompt lasting visual impairment. There are no particular side effects when the glaucoma disease is considered, especially for this type of eye disease, the effect of which is the vision loss in the human eyes. Because of measuring, the container zone increments, which will result in the vision impairment in the human eyes. Normally exceptionally prepared opthalmogists physically review eye pictures as tedious way. In this unique circumstance, we are attempting to build up some novel calculations for programmed recognition of eyes influenced with glaucoma utilizing picture preparing separating and change strategies and actualize the same on equipment utilizing micro-controller framework. The product that will be created by us could be implanted on the equipment to test the sound and undesirable fundus pictures for the recognition of glaucoma. The calculations that could be created can be actualized wrt the eye pictures in HDL language utilizing Xilinx ISE, MATLAB and MODELSIM, TI based unit or NI based pack (any one) is the equipment apparatus that is considered for execution purposes.
Glaucoma is the most leading cause of irreversible blindness with the population of Africa and Asia ranking the highest over the rate of glaucoma affected regions around the world. The defect will damage eyes irreversibly by affecting the optic cup and optic disc of an eye. The early detection of glaucoma is an unavoidable need in the medical field. The widely used technique to detect glaucoma is an invasive method that may lead to other effects on the eye. This reason led to the introduction of a non invasive method that follows image processing for the detection of glaucoma. Retinal image based detection is the best way to choose as it comes under non invasive methods of detection. Detection of glaucoma using retinal images requires various medical features of the eyes such as optic cup diameter, optic disc diameter and optic cup to disc ratio are used. Glaucoma disease detection from retinal images supports convolutional neural networks CNN . The textual features obtained from retinal images such as the optic cup to optic disc measures are used for this classification. Convolutional Neural Networks use little pre processing techniques that can be implemented relatively uncomplicated compared to other image classification techniques. The implementation of this project follows the traditional CNN architecture, applying filter layers such as Convolution layer and Pooling layer and also activation functions such as ReLu function and sigmoid function to pre process as well as to update weights respectively on the hidden layers of the CNN followed by classifying the image. Vishnubhotla Poornasree | Vijayagiri Ashritha | Venumula Deeksha Reddy | J. Srilatha ""Glaucoma Detection from Retinal Images"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23732.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23732/glaucoma-detection-from-retinal-images/vishnubhotla-poornasree
Glaucoma Detection in Retinal Images Using Image Processing Techniques: A SurveyEswar Publications
Glaucoma is a disease associated with human eyes and second conducting movement o fblindness across the globe if
eyes are not treated at preliminary stage. Glaucoma normally occurs with increased intra-ocular pressure (IOP) in eyes and gradually damagesthe vision field of eyes. The term ocular-hypertension is related to those people in whom IOP increases consistently and does not damage the optic nerve. Glaucoma has different types such as open-angle, close-angle, congenital, normal tension and etcetera. Normal tension glaucoma affects vision field and damages optic nerve as well. The term angle means the distance between iris and cornea; if this distance is large it is referred to as open-angle glaucoma and similarly if the distance between iris and cornea is short than this is called close-angle glaucoma. Open-angle glaucoma is common as compared to close-angle glaucoma. Close-angle glaucoma is very painful and affects vision field of eyes quickly as compared to open-angle glaucoma. In this
paper, the state of the art CAD systems and image processing methods are studied and compared systematically in terms of their classification accuracy, methodology approach, sensitivity and specificity. The comparison results indicate that the accuracy of these CAD systems and image processing methods is not up to the mark.
FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR sipij
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of thedisease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which isthe ratio of the vertical diameter of the optic cup to that of the optic disc, of the fundus eye image is an important clinical indicator for glaucoma diagnosis. This paper presents an automated method for the extraction of optic disc and optic cup using Fuzzy C Means clustering technique combined with
thresholding. Using the extracted optic disc and optic cup the vertical cup-to-disc ratio was calculated.
The validity of this new method has been tested on 365 colour fundus images from two different publicly
available databases DRION, DIARATDB0 and images from an ophthalmologist. The result of the method
seems to be promising and useful for clinical work.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Detection of Glaucoma using Optic Disk and Incremental Cup Segmentation from ...theijes
Medical researchers, detection of eye disease is very important because it may causes blindness. Glaucoma is one of the diseases that cause blindness. Standard procedure for detection glaucoma is to analysis of optic disk (OD) and cup region in retinal image. In this paper, introduce an automatic OD parameterized technique which is based on segmentation and Incremental Cup segmentation. The incremental cup segmentation method is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is applied to derive a reliable subset of vessel bends called r-bends followed by a local 2-D spline fitting to derive the desired cup boundary. The results are compared with existing methods using different retinal images.
Development of novel BMIP algorithms for human eyes affected with glaucoma an...Premier Publishers
Glaucoma is one of the second driving eye maladies on the planet, if not treated legitimately may prompt lasting visual impairment. There are no particular side effects when the glaucoma disease is considered, especially for this type of eye disease, the effect of which is the vision loss in the human eyes. Because of measuring, the container zone increments, which will result in the vision impairment in the human eyes. Normally exceptionally prepared opthalmogists physically review eye pictures as tedious way. In this unique circumstance, we are attempting to build up some novel calculations for programmed recognition of eyes influenced with glaucoma utilizing picture preparing separating and change strategies and actualize the same on equipment utilizing micro-controller framework. The product that will be created by us could be implanted on the equipment to test the sound and undesirable fundus pictures for the recognition of glaucoma. The calculations that could be created can be actualized wrt the eye pictures in HDL language utilizing Xilinx ISE, MATLAB and MODELSIM, TI based unit or NI based pack (any one) is the equipment apparatus that is considered for execution purposes.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Glaucoma Screening Test By Segmentation of Optical Disc& Cup Segmentation Usi...IJERA Editor
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering
the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is
essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We
devised a novel, automated, appearance based glaucoma classification system that does not depend on
segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening
examinations. The proposed segmentation methods have been evaluated in a database of 650 images with optic
disc and optic cup boundaries manually marked by trained professionals. Our expected Experimental results
may be average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
An Approach for the Detection of Vascular Abnormalities in Diabetic Retinopathyijdmtaiir
Diabetic Retinopathy is a common complication of
diabetes that is caused by changes in the blood vessels of the
retina. The blood vessels in the retina get altered. Exudates are
secreted, micro-aneurysms and hemorrhages occur in the
retina. The appearance of these features represents the degree
of severity of the disease. In this paper the proposed approach
detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing
techniques to the fundus images to extract features such as
blood vessels, micro aneurysms and exudates. These features
are used for the detection of severity of Diabetic Retinopathy.
It can quickly process a large number of fundus images
obtained from mass screening to help reduce the cost, increase
productivity and efficiency for ophthalmologists.
Segmentation of the Blood Vessel and Optic Disc in Retinal Images Using EM Al...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
Automatic identification and classification of microaneurysms for detection o...eSAT Journals
Abstract Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. He experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, we have fabricated a prototype of automatic headlight dimmer. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. It also eliminates the requirement of manual switching by the driver which is not done at all times. The construction, working and the advantages of this prototype model is discussed in detail in this paper. Keywords: Headlight, automatic, dimmer, control, high beam, low beam, Kelvin (K).
Automatic Detection of Diabetic Maculopathy from Funduas Images Using Image A...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
Performance analysis of retinal image blood vessel segmentationacijjournal
The retinal image diagnosis
is an important methodology for diabetic retinopathy detection and analysis. in
this paper, the morphological operations and svm classifier are used to detect and segment the blood
vessels from the retinal image. the proposed system consists of three stage
s
-
first is preprocessing of retinal
image to separate the green channel and second stage is retinal image enhancement and third stage is
blood vessel segmentation using morphological operations and svm classifier. the performance of the
proposed system is
analyzed using publicly available dataset
Optic Disc and Macula Localization from Retinal Optical Coherence Tomography ...IJECEIAES
This research used images from Optical Coherence Tomography (OCT) examination as well as fundus images to localize the optical disc and macular layer of retina. The researchers utilized the OCT and fundus image to interpret the distance between macular center and optic disc in the image. The distance will express the area of macula that can be employed for further research. This distance could recognize the thickness of macula parameters diameter that will be used in localizing process of optic disc and macula. The parameters are the circle radius, the size of window’s filter, the constant value and the size of optic disc element structure as well as the size of macula. The results of this study are expected to improve the accuracy of macula detection that experience the edema.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Glaucoma Screening Test By Segmentation of Optical Disc& Cup Segmentation Usi...IJERA Editor
Glaucoma is one of the most common causes of blindness and it is becoming even more important considering
the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is
essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We
devised a novel, automated, appearance based glaucoma classification system that does not depend on
segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening
examinations. The proposed segmentation methods have been evaluated in a database of 650 images with optic
disc and optic cup boundaries manually marked by trained professionals. Our expected Experimental results
may be average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively.
Diabetic retinopathy is a disease, caused by alternation in the retinal blood vessels. It is a strong sign of early blindness and if it is not treated may tend to complete blindness and the vision lost once cannot be restored once again. In this paper different image processing techniques are used to differentiate between the normal and the diseased image. The attempt is made to see where the problem actually lies so that proper diagnosis of patient can be done. Pre processing of an image, optic disk detection, Blood vessels extraction, Exudates detection are some of the methods that are applied here. Other algorithms are designed to obtain the desired result. A large number of populations are affected by this disease around the world.
An Approach for the Detection of Vascular Abnormalities in Diabetic Retinopathyijdmtaiir
Diabetic Retinopathy is a common complication of
diabetes that is caused by changes in the blood vessels of the
retina. The blood vessels in the retina get altered. Exudates are
secreted, micro-aneurysms and hemorrhages occur in the
retina. The appearance of these features represents the degree
of severity of the disease. In this paper the proposed approach
detects the presence of abnormalities in the retina using image
processing techniques by applying morphological processing
techniques to the fundus images to extract features such as
blood vessels, micro aneurysms and exudates. These features
are used for the detection of severity of Diabetic Retinopathy.
It can quickly process a large number of fundus images
obtained from mass screening to help reduce the cost, increase
productivity and efficiency for ophthalmologists.
Segmentation of the Blood Vessel and Optic Disc in Retinal Images Using EM Al...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
Automatic identification and classification of microaneurysms for detection o...eSAT Journals
Abstract Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. He experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, we have fabricated a prototype of automatic headlight dimmer. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. It also eliminates the requirement of manual switching by the driver which is not done at all times. The construction, working and the advantages of this prototype model is discussed in detail in this paper. Keywords: Headlight, automatic, dimmer, control, high beam, low beam, Kelvin (K).
Automatic Detection of Diabetic Maculopathy from Funduas Images Using Image A...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
Performance analysis of retinal image blood vessel segmentationacijjournal
The retinal image diagnosis
is an important methodology for diabetic retinopathy detection and analysis. in
this paper, the morphological operations and svm classifier are used to detect and segment the blood
vessels from the retinal image. the proposed system consists of three stage
s
-
first is preprocessing of retinal
image to separate the green channel and second stage is retinal image enhancement and third stage is
blood vessel segmentation using morphological operations and svm classifier. the performance of the
proposed system is
analyzed using publicly available dataset
Optic Disc and Macula Localization from Retinal Optical Coherence Tomography ...IJECEIAES
This research used images from Optical Coherence Tomography (OCT) examination as well as fundus images to localize the optical disc and macular layer of retina. The researchers utilized the OCT and fundus image to interpret the distance between macular center and optic disc in the image. The distance will express the area of macula that can be employed for further research. This distance could recognize the thickness of macula parameters diameter that will be used in localizing process of optic disc and macula. The parameters are the circle radius, the size of window’s filter, the constant value and the size of optic disc element structure as well as the size of macula. The results of this study are expected to improve the accuracy of macula detection that experience the edema.
Glaucoma is a chronic eye disease in which the optic nerve head is progressively damaged which leads to loss of
vision. Early diagnosis and treatment is the key to preserving sight in people with glaucoma. Current tests using
intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Assessment of the
damaged optic nerve head is both more promising, and superior to IOP measurement or visual field testing. This paper
presents superpixel classification based optic disc and optic cup segmentation for glaucoma screening. In optic disc
segmentation, histograms and centre surround statistics are used to classify each superpixel as disc or non-disc. For optic
cup segmentation, in addition to the histograms and centre surround statistics, the location information is also included
into the feature space to boost the performance. The segmented optic disc and optic cup are used to compute the CDR
for glaucoma screening. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier
to confirm Glaucoma given patient.
Keywords — IOP measurement, optic cup segmentation, optic disc segmentation, CDR.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The legal cause of blindness for the workingage
population in western countries is Diabetic Retinopathy - a
complication of diabetes mellitus - is a severe and wide- spread
eye disease. Digital color fundus images are becoming
increasingly important for the diagnosis of Diabetic Retinopathy.
In order to facilitate and improve diagnosis in different ways, this
fact opens the possibility of applying image processing techniques
.Microaneurysms is the earliest sign of DR, therefore an
algorithm able to automatically detect the microaneurysms in
fundus image captured. Since microaneurysms is a necessary
preprocessing step for a correct diagnosis. Some methods that
address this problem can be found in the literature but they have
some drawbacks like accuracy or speed. The aim of this thesis is
to develop and test a new method for detecting the
microaneurysms in retina images. To do so preprocessing, gray
level 2D feature based vessel extraction is done using neural
network by using extra neurons which is evaluated on DRIVE
database which is superior than rulebased methods. To identify
microaneurysms in an image morphological opening and image
enhancement is performed. The complete algorithm is developed
by using a MATLAB implementation and the diagnosis in an
image can be estimated with the better accuracy and in shorter
time than previous techniques
Diabetic retinopathy is the cause for
blindness in the human society. Early detection of it prevents
blindness. Image processing techniques can reduce the work of
ophthalmologists and the tools used to detect Diabetic
Retinopathy Patients. Proliferative diabetic retinopathy is the
most advanced stage of diabetic retinopathy, and is classified by
the growth of new blood vessels. These blood vessels are
abnormal and fragile, and are susceptible to leaking blood and
fluid onto the retina, which can cause severe vision loss. First,
vessel-like patterns are segmented by using Ridge Strength
Measurement and Watershed lines. The second step is measuring
the vessel pattern obtained [5][10]. Many features that are
extracted from the blood vessels such as shape, position,
orientation, brightness, contrast and line density have been used
to quantitative patterns in retinal vasculature. Based on the seven
features extracted, the segment is classified as normal or
abnormal by using Support Vector Machine Classifier [6][8]. The
obtained accuracy may be sufficient to reduce the workload of an
ophthalmologist and to prioritize the patient grading queues.
Automated Screening of Diabetic Retinopathy Using Image Processingiosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...iosrjce
To diagnosis of Diabetic Retinopathy (DR) it is the prime cause of blindness in the working age
population of the world. Detection method is proposed to detect dark or red lesions such as microaneurysms
and hemorrhages in fundus images.Developed during this work, this first is for collection of lesion data
information and was used by the ophthalmologist in marking images for database while the automatic
diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter
three of this report. The primary aim of this project is to develop a system that will be able to identify patients
with BDR and PDR from either colour image or grey level image obtained from the retina of the patient. The
algorithm was tested fundus images. The Operating Characteristics (ROC) was determined for red spot lesion
and bleeding, while cross over points were only detected leaving further classification as part of future work
needed to complete this global project. Sensitivity and specificity was calculated for the algorithm is given
respectively as 96.3% and 95.1%
Detection and Grading of Diabetic Maculopathy Automatically in Digital Retina...paperpublications3
Abstract: Diabetic Retinopathy (DR) is a critical eye disease which can be regarded as manifestation of diabetes on the retina the symptoms can blur or distort the patient’s vision and are a main cause of blindness. Exudates are one of the signs of Diabetic Retinopathy. If the disease is detected early and treated promptly many of the visual loss can be prevented. This paper explains the development of an automatic fundus image processing and analytic system to facilitate diagnosis of the ophthalmologists. The algorithms to detect the optic disc; blood vessels and exudates are investigated. The proposed system extracts macula from digital retinal image using the optic disc location. Many common features such as intensity, geometric and correlations are used to distinguish between them. The system uses GLCM for feature extraction. The system uses a SVM based classifier to differentiate the retinal images in different stages of maculopathy by using the macula co-ordinates and exudates feature set.
An Efficient Integrated Approach for the Detection of Exudates and Diabetic M...acijjournal
Diabetic Retinopathy (DR) is a major cause of blindness. Exudates are one of the primary signs of diabetic retinopathy which is a main cause of blindness that could be prevented with an early screening process In this approach, the process and knowledge of digital image processing to diagnose exudates
from images of retina is applied. An automated method to detect and localize the presence of exudates and Maculopathy from low-contrast digital images of Retinopathy patient’s with non-dilated pupils is proposed. First, the image is segmented using colour K-means Clustering algorithm. The segmented image along with Optic Disc (OD) is chosen. To Classify these segmented region, features based on colour and texture are extracted. The selected feature vector are then classified into exudates and nonexudates using a Support Vector Machine (SVM) Classifier. Also the detection of Diabetic Maculopathy,
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A UTOMATIC C OMPUTATION OF CDR U SING F UZZY C LUSTERING T ECHNIQUEScsandit
Eye disease identification techniques are highly im
portant in the field of ophthalmology. A
vertical Cup-to-Disc Ratio which is the ratio of th
e vertical diameter of the optic cup to that of
the optic disc, of the fundus eye image is one of t
he important signs of glaucoma. This paper
presents an automated method for the extraction of
optic disc and optic cup using Fuzzy C
Means clustering technique. The validity of this ne
w method has been tested on 454 colour
fundus images from
three different publicly available databases DRION,
DIARATDB0 and
DIARETDB1 and, images from an ophthalmologist. The
average success rate of optic disc and
optic cup segmentation is 94.26percentage. The scat
ter plot depicts high positive correlation
between clinical CDR and the CDR obtained using the
new method. The result of the system
seems to be promising and useful for clinical work
A UTOMATIC C OMPUTATION OF CDR U SING F UZZY C LUSTERING T ECHNIQUEScsandit
Eye disease identification techniques are highly im
portant in the field of ophthalmology. A
vertical Cup-to-Disc Ratio which is the ratio of th
e vertical diameter of the optic cup to that of
the optic disc, of the fundus eye image is one of t
he important signs of glaucoma. This paper
presents an automated method for the extraction of
optic disc and optic cup using Fuzzy C
Means clustering technique. The validity of this ne
w method has been tested on 454 colour
fundus images from
three different publicly available databases DRION,
DIARATDB0 and
DIARETDB1 and, images from an ophthalmologist. The
average success rate of optic disc and
optic cup segmentation is 94.26percentage. The scat
ter plot depicts high positive correlation
between clinical CDR and the CDR obtained using the
new method. The result of the system
seems to be promising and useful for clinical work.
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SURVEY OF GLAUCOMA DETECTION METHODS
1. Scientific Journal Impact Factor (SJIF): 1.711
International Journal of Modern Trends in Engineering
and Research
www.ijmter.com
@IJMTER-2014, All rights Reserved 197
e-ISSN: 2349-9745
p-ISSN: 2393-8161
SURVEY OF GLAUCOMA DETECTION METHODS
Pranchal Chaudhari1
, Rupali Nikhare2
1
Information Technology,PIIT,Panvel
2
Assistant Professor, Information Technology, PIIT,Panvel
Abstract- GLAUCOMA is a chronic eye disease that can damage optic nerve. According to WHO It
is the second leading cause of blindness, and is predicted to affect around 80 million people by 2020.
Development of the disease leads to loss of vision, which occurs increasingly over a long period of
time. As the symptoms only occur when the disease is quite advanced so that glaucoma is called the
silent thief of sight. Glaucoma cannot be cured, but its development can be slowed down by
treatment. Therefore, detecting glaucoma in time is critical. However, many glaucoma patients are
unaware of the disease until it has reached its advanced stage. In this paper, some manual and
automatic methods are discussed to detect glaucoma. Manual analysis of the eye is time consuming
and the accuracy of the parameter measurements also varies with different clinicians. To overcome
these problems with manual analysis, the objective of this survey is to introduce a method to
automatically analyze the ultrasound images of the eye. Automatic analysis of this disease is much
more effective than manual analysis.
Keywords-Glaucoma, Optic cup segmentation, Optic disc segmentation, cup-to-disc ratio, active
contour, vessel bend.
I. INTRODUCTION
Glaucoma is the second leading cause of blindness that can damage the eye’s optic nerve, resulting
in loss of vision and thereby cause permanent blindness. Though there is no cure, early diagnosis
with adequate medication and care, it is possible to stop further impact on vision to a patient.With
the predicted increase in life expectancy, the anticipated number of people becoming blind from the
disease will rise substantially in the near future. Despite increasing public health awareness and the
availability of advanced technology diagnostic tests in developed countries.
Figure 1. Normal eye and glaucoma effected eye
2. International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 02, Issue 01, [January - 2015] e-ISSN: 2349-9745, p-ISSN: 2393-8161
@IJMTER-2014, All rights Reserved 198
There are three methods to detect glaucoma
1) Assessment of raised intraocular pressure (IOP).
2) Assessment of abnormal visual field.
3) Assessment of damaged optic nerve head.
The IOP measurement using noncontact tonometry is neither specific nor sensitive enough to be an
effective screening tool because glaucoma can be present with or without increased IOP.Visual field
testing requires special equipment that is usually present only in hospitals. It is a subjective examination
as it assumes that patients fully understand the testing instructions, cooperate and complete the test.
Moreover, the test is usually time consuming. Thus, the information obtained may not be reliable.
The assessment of optic nerve damage is superior to the other two methods. Optic nerve can be
assessed by trained specialists or through 3D imaging techniques such as Heidelberg Retinal
Tomography (HRT) and Ocular Computing Tomography (OCT). However, optic nerve assessment
by specialists is subjective and the availability of HRT and OCT equipment is limited due to the high
cost involved.
An automatic and economic system is highly desirable for detection of glaucoma in large-scale
screening programs. The digital color fundus image is a more cost effective imaging modality to
assess optic nerve damage compared to HRT and OCT, and it has been widely used in recent years to
diagnose various ocular diseases, including glaucoma. An ophthalmologist will diagnose Glaucoma
by measuring the CDR (Cup to Disc Ratio) which is the ratio of the vertical height of the optic cup
and optic disc.
II. GLAUCOMA DETECTION METHODS
Glaucoma is a disease characterized by degeneration of optic nerves. So the fall in blood flow to the
optic nerve give to the visual field defects associated with glaucoma. Drug therapy to control the
elevated intraocular pressure and serial evaluation of the optical nerves are the principal method of
curing the disease. Standard methods of evaluation of the optic nerve using ophthalmology or stereo
photography or evaluation of visual fields. There are manual and automatic detection methods
available. The survey is conducted on different glaucoma detection methods in image processing.
This section briefly describes some of the techniques that are used for the detection of glaucoma.
Figure 2. Classification of Glaucoma Detection Methods
Quantitative
Glaucoma Detection Methods
Qualitative
Confocal Scanning Laser
Ophthalmology
Scanning Laser Polarimetry
Optical Coherence Tomography
Automated Glaucoma Detection by using
CDR
Optic Disc and Optic Cup Segmentation
from Monocular color Retinal Images For
Glaucoma Assessment
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A. Automated Glaucoma Detection by using CDR
The diagnosis of glaucoma can be done through measurement of CDR (cup-to-disc ratio). Currently,
CDR evaluation is manually performed by trained ophthalmologists or expensive equipment such as
Heidelberg Retinal Tomography (HRT). However, CDR evaluation by an ophthalmologist is
subjective and the availability of HRT is very limited.
In [7] this method CDR is calculated automatically from nonsterographic retinal fundus photographs.
To automatically extract the disc, two methods making use of an edge detection method and
variational level-set method are proposed. For the cup, color component analysis and threshold level-
set method are evaluated. To reshape the obtained disc and cup boundary from above mentationed
methods, ellipse fitting is applied to the obtained image.
Methodology
To calculate the vertical cup to disc ratio (CDR), the optic cup and disc first have to be segmented
from the retinal images. Figure 3 shows the framework for building the glaucoma detection system.
Figure 3. Framework for building the glaucoma detection system.
In order to extract the optic disc and cup, each retinal fundus image has been captured using a high
resolution retinal fundus camera and saved as a 3072 x 2048 high-resolution digital image, as shown
in Figure (a). Thus, the region of interest (ROI) around the optic disc must first be delineated.
The set of fundus images are firstly examined, and it is found that the optic disc region is usually of a
brighter pallor or higher color intensity than the surrounding retinal area. The fundus images with the
highest intensity are selected as potential candidates for the optic disc center, as shown in Figure (b).
The intensity-weighted centroid method is proposed to find an approximate ROI centre. The
boundary of the ROI is defined as a rectangle around the ROI centre with dimensions of twice the
typical optic disc diameter, and is used as the initial boundary for the optic disc segmentation, as
shown in Figure (c). The ROI is returned as an image of size 480x750 pixels as shown in Figure (d).
Optic nerve image
ROI Detection
Disc segmentation Cup Segmentation
Disc boundary smoothing Cup boundary smoothing
Calculate CDR
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Figure (a) Figure (b)
Figure (c) Figure (d)
Figure 4. a) Input image of size 3072 x 2048 pixels b) A brighter pallor detected (blue area) c) ROI localization d)
ROI image of size 480 x 750 pixels.
1. Optic Disc Segmentation
To automatically extract an optic disc boundary, image pre-processing is introduced. Figure 5 shows
a simplified workflow of optic disc segmentation.
Figure 5. Optic Disc Segmentation
A coarse localization of optic disc region is presented using the red channel. The red component is
utilized as it is found to have higher contrast between the optic disc and non-optic disc area than for
Input Image
Image Pre-processing
Edge Detection by Canny Algorithm Variational Level-Set
K-mean Clustering
Ellipse Fitting
Ellipse Fitting
Compared Result
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other channels. To remove the blood vessels, a morphological closing operation is performed. After
performing the closing operation, a median filter is applied to further smoothen the obtained image.
The outputs of the image pre-processing are shown in Figure 6
(a) (b) (c) (d)
Figure 6. a) Input Image b) Red channel c) Closing operation and d) Median filter
1.2 Edge Detection Approach
The Canny method is specified for edge detection because the Canny algorithm can detect edges
with noise suppressed at the same time. This method uses two thresholds, to detect strong and weak
edges, and it includes the weak edges in the output only if they are connected to strong edges. The
optimum threshold of the each input retinal image is found to be different due to the variant
intensities in each image.
In order to extract only a disc boundary, the edge image has to be classified into three groups based
on the distance from the center of the optic disc as shown in below Figure 7(a). This classification is
achieved by performing k-means clustering to the edge of the image 7(b). K-means clustering is a
method of cluster analysis which aims to partition n observations into k clusters in which each
observation belongs to the cluster with the nearest mean. Then, the group that contains only edge
detection of a disc boundary is selected, and the noise can be rejected as shown in Figure 7(c).After
that, the application of direct ellipse fitting is used to obtain boundaries in order to find a smoother
contour. The outputs are shown in Figure 7(d).
(a) (b) (c) (d)
Figure 7. a) Input image after performing the image pre-processing with the center of an optic disc b) Edge detection
c) The result after noise is rejected d) Disc boundary smoothing.
1.3 Variational Level-Set Approach
The variational level- set algorithm is used as a global approach for the optimization of active
contours for the segmentation of objects of interest from the background. This method is employed
by initializing a curve centered at the detected optic disc location. The curve is evolved based on the
average intensity value inside and outside the curve. The curve evolution always converges to the
optic disc boundary irrespective of the shape or size of the initial contour.
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(a) (b) (c) (d)
Figure 8. Sample Evolution Results at Different Iterations
2. Optic Cup Segmentation
Optic Cup Segmentation, two approaches are used .The color component analysis approach and
threshold level set approach.
2.1 Color Component Analysis Approach.
In the color component analysis method, RGB components of the input images are analyzed, and it is
found that the optic cup is more easily discriminated in the green image because the visibility and
contrast of the optic cup is superior and its pixels are of higher intensities, while the neuroretinal rim
and the retinal vessels are often of lower intensities. The next step is to use morphological opening
operation in order to remove noise around the cup region. Then the edge detection and ellipse fitting
is proposed to obtain the cup boundary smoothing.
(a) (b) (c) (d) (e)
Figure 9. a) Input Image b) Color Green Detected c) Morphological Opening Operation d) Edge Detection
e) Cup Boundary Smoothing
2.2 Threshold Level Set Approach
In this approach, the green channel of the input image is selected as the basis for further
segmentation due to the optimum observed contrast between the cup and disc boundaries in this
channel. The method to select the top 1/3 of the grayscale intensity is to find the threshold value
from the normalized cumulative histogram and then compare it with all the intensity values of the
input image. Then an intensity value is that is greater than the threshold value is selected.Text step is
to use a morphological opening operation in order to remove noise around the cup region.Next, the
intensity-weighted centroid method is proposed to find an approximate initial point. This is found to
give a good initial approximation for the initial cup region. Then, a threshold level-set algorithm is
applied to segment the optic cup.
(a) (b) (c) (d) (e)
Figure 10. A) Input Image B) Color Green Detected C) Morphological Opening Operation D) The Initial Point Of
Cup Region E) The Initial Cup Contour.
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3. Ellipse Fitting for Optic Disc and Cup
Ellipse fitting algorithm can be used to smooth the optic cup and disc boundary. Ellipse fitting is
usually based on the least square fitting algorithm which assumes that the best-fit curve of a given
type is the curve that has the minimal sum of the deviations squared from, given data points. Direct
Least Square Fitting Algorithm is chosen to fit the optic disc from popular ellipse fitting algorithms.
It is ellipse specific, thus the effect of noise (ocular blood vessel, hemorrhage, etc.) around the disc
area can be minimized while forming the ellipse. It can also be easily solved naturally by a
generalized Eigen system.
4. Calculate CDR
The ratio of the size of the optic cup to the optic disc, also known as the cup-to-disc ratio (CDR), the
value of CDR which is more than 0.65 is used to assess a patient as a possible glaucoma case.
B. Optic Disc and Optic Cup Segmentation from Monocular Color
Retinal Images for Glaucoma Assessment
Interest in OD segmentation is not limited to glaucoma detection. It is a fundamental task for
automatic processing of retinal images such as image sequence registration, and automatic
measurements for treatment evaluation or for diabetic retinopathy diagnosis. Initial attempts have
been made with shape-based template matching in which OD is modelled as a circular or elliptical
object. This matching is performed on an edge map extracted from the underlying image. This
approach suffers due to vessel edges present in and around the OD region. To handle this,
morphological-based pre-processing step is employed to suppress the vessel prior to template
matching[1].
1. Localised and vector-valued C-V active contour model
This model assumes that an image consists of statistically homogeneous regions and therefore lacks
the ability to deal with objects having intensity inhomogeneity. Intensity inhomogeneity is very
common in natural images, especially in OD region it is a frequently occurring phenomena. In
computer vision, there have been some attempts to improve C-V model for such situations. Here, the
basic idea is to use local instead of global image intensity into the region-based active contour
model.
These methods report significant improvement in the segmentation over original C-V model for
segmenting objects with heterogeneous intensity statistics. However other than intensity
heterogeneity within OD, smooth region transition at boundary locations and occurrence of similar
characteristic regions near the OD boundaries (atrophy) make OD segmentation a much more
difficult case altogether. The local intensity based statistics is not sufficient to discriminate between
the OD and atrophy regions. We propose a region-based active contour model which uses local
image information at a support domain around each point of interest (POI) inspired by localised C-V
models by using a richer form of local image information gathered over a multi-dimensional feature
space. The intention is to represent the POI more holistically by including descriptions of the
intensity, colour, texture, etc. This approach should yield a better representation of image regions and
make the proposed model robust to the distractions found near the OD boundaries.
2. OD localisation and contour initialisation
The first step is to localise the OD region and extract a region of interest for further processing. The
red colour plane of CFI gives good definition of OD region and thus is a good choice for the OD
localisation task. The contour initialization is the next essential step to initiate the active contour
evolution. In our method, we perform localisation and initialisation steps together by performing
circular Hough transform on the gradient map.
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The vessel segments are identified using a curvature-based technique. These regions are suppressed
and inpainted by performing selective morphological closing in 8 directions and retaining maximum
response for each vessel pixel. Next, a Canny edge detector at a very low threshold is applied on the
pre-processed (vessel-free) image to get edge points. On these points, a circular Hough transform is
applied for a range of expected OD radius (rmin to rmax). This range is chosen based on the retinal
image resolution. We select the OD center which has maximum value in the accumulator matrix
while performing Circular Hough transform. 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 initialise the active contour mentioned in section
Figure 11. Sample Results of C-V Active Contour.
2.1 Segmentation in multi-dimensional feature space
A multi-dimensional image representation is obtained from colour and texture feature space. In
normal image conditions, red colour plane gives a better contrast of the OD region. To better
characterise OD in pathological situations, two different texture representations are derived.
3. CUP Segmentation
The objective is to segment the cup region by using both vessel bends and pallor information. vessel
bends can occur at many places within the OD region. However, only a subset of these points
define the cup boundary. It refer to this as relevant vessel bends or r-bends. The first problem at hand
is to find this subset. We use multiple sources of information for this purpose: the pallor region
which spatially defines the inner limit of r-bends, bending angle and location in the OD region.
3.1 Cup segmentation using r-bends information
The objective is to segment the cup region by using both vessel bends and pallor information. cyan
points in figure 13, vessel bends can occur at many places within the OD region. However, only a
subset of these points defines the cup boundary. this is relevant vessel bends or r-bends. The first
problem at hand is to find this subset, multiple sources of information is use for this purpose: the
pallor region which spatially defines the inner limit of r-bends, bending angle and location in the OD
region.
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 r- bends are absent.
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Figure 12. The Proposed Cup Segmentation Method
Figure 13. a) Angle of a vessel bend, b) uniform pallor samples(red),
bend points(cyan) and c) fitted circle(red) and potential r-bends
3.1.1 Medial axis detection
The OD region has both thick and thin vessels. Detecting both reliably is difficult in the presence of
inter-image intensity variations. This method formulates the blood vessel detection as a problem of
trench detection in the intensity surface. The selection of this space gives robustness to the image
variations and detection is solely driven by the shape of trench and directional continuity associated
with a vessel structure. Trenches are regions characterized by high curvature, oriented in a particular
direction.
3.1.2 Vessel Bend detection
The amount of bending in vessels varies according to the caliber of vessel. Thin vessels show
significant bending compared to a thick vessel. This is due to the fact that thick vessels are more
rigid. The selection of appropriate scale for detecting bends in both types of vessels is crucial
because bend in a thick vessel is apparent only at a larger scale compared to a bend in thin vessel.
We employ a scheme based on the concept of dynamic region of support (ROS) which has been
proposed for corner detection to find the appropriate scale to analyse a candidate point. This is
explained below.
First, we extract vessel segments terminated by end and/or junction points. For each segment, we
compute 1D shape (curvature) profile and locate the local maxima. These local maxima constitute a
candidate set of bends bi. A ROS for any bi is defined as a segment of vessel around bi and bound on
either side by the nearest curvature minimum. Choosing the bounds to be based on curvature minima
automatically ensures the size of the ROS to be large for thick vessels and small for thin vessels.
The angle of bend θ is then computed as the angle between the lines joining a bend point and the
center of mass on both sides of the ROS. The center of mass of an arm is defined by the mean
position of pixels on the arm. Since only vessels bending into the cup are of interest, bends above θ =
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170◦ are eliminated from the candidate set. The detected vessel bends in a sample image are
highlighted in figure(13) with cyan markers.
3.1.3 Multi-stage selection of r-bends
The task of identifying the r-bends from bi is performed in two stages to reduce the required
analysis, by utilizing anatomical knowledge associated with r-bends. In the first stage, a coarse
selection is done based on a bend’s proximity to the pallor region. In the second stage, the spatial
position and bending information are used to identify the set of r-bends.
(a) (b)
Figure 14 a)Estimated Cup Boundry b)Final Cup Boundry
3.1.4 2D spline interpolation
Typically, r-bends are sparse and not uniformly distributed across the sectors. In their absence,
experts use their clinical knowledge (experience of direct 3D cup examination) to approximate a cup
boundary. Hence, it is difficult to get the cup boundary in the regions with no r-bends. We choose a
local, cubic cardinal spline, which is a generalisation of Catmull-Rom spline, with a shape parameter
t. The parameter t helps control the bending behaviour and thus the shape according to the sector.
The value of t is kept high in sectors 2&4 as they usually have low vessel density (r-bends)
compared to sector 1&3. A closed-form 2D spline curve is obtained by considering, sequentially a
subset of r-bends. Figure 7(a) shows the interpolated cup boundary passing through the r-bends and
Fig. 7(b) shows final obtained boundaries for a sample OD region.
C. Confocal Scanning Laser Ophthalmology
In [6] Confocal scanning laser ophthalmoscopy (CSLO), a laser based image gaining which is
proposed to improve the quality of the examination compared to ordinary ophthalmologic
examination. A laser id scanned across the retina along with a detector system. Once a single spot on
the retina is illuminated at any time, ensuing in a high-contrast image of great reproducibility that
can be used to estimate the width of the RNFL. In addition, this technique does not need maximal
mydriasis, which may be a problem in patients having glaucoma. The Heidelberg Retinal
Tomography is possibly the most common example of Confocal scanning laser ophthalmoscopy
(CSLO).
Figure 15. HRT Machine
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D. Scanning Laser Polarimetry
RNFL is birefringent, which causes a change in the state of divergence of a laser beam as it passes. It
uses a 780-nm diode to illuminate optic nerve. The polarization state of the light emerging from the
eye is then evaluated and linked with RNFL thickness. Unlike CSLO, scanning laser polarimetry
(SLP) can unswervingly measure the thickness of the RNFL[6]. GDx® is an ordinary example of a
scanning laser polarimeter. GDx®contain a normative database and statistical software package to
permit comparison to age-matched normal subjects of the same racial origin.
The advantages of this system are that images can be obtained without pupil dilation, and evaluation
can be done roughly in 10 minutes. Modern instruments have added improved and erratic corneal
compensation technology to account for corneal polarization.
Figure 16. GDx VCC
E. Optical Coherence Tomography
Optical coherence tomography (OCT) uses near-infrared light to provide direct crosssectional
measurement of the RNFL [6]. The principles employed are alike to those used in B-mode
ultrasound except light, not sound, is used to create the 2-dimensional images.
The light source can be directed into the eye through a conservative slit-lamp biomicroscope and
focused on the retina through a distinctive 78-diopter lens. This system requires dilation of the
patient’s pupil. OCT is an example of this technology.
Figure 17. OCT
III. CONCLUSION
Glaucoma is a silent disease that comes with no symptoms and warning. Initially no one can say that
the patient is having any sort of problem either by looking and touching the eye. Hence its detection
and diagnosis are very essential.