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.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
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.
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.
Resultados preliminares do implante de um novo anel associado ao PRK para pre...Ferrara Ophthalmics
Dr. Sandro Coscarelli, Dr. Pablo Rodrigues, Dr. Guilherme Rocha e Dr. Leonardo Torquetti compilaram e compartilham seus resultados com o uso de Segmentos de Anel de Ferrara HM associado ao PRK para a correção da miopia de pacientes com corneas finas e contra indicados para as técnicas de Excimer Laser apenas.
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).
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.
Retinal image analysis using morphological process and clustering techniquesipij
This paper proposes a method for the Retinal image analysis through efficient detection of exudates and
recognizes the retina to be normal or abnormal. The contrast image is enhanced by curvelet transform.
Hence, morphology operators are applied to the enhanced image in order to find the retinal image ridges.
A simple thresholding method along with opening and closing operation indicates the remained ridges
belonging to vessels. The clustering method is used for effective detection of exudates of eye. Experimental
result proves that the blood vessels and exudates can be effectively detected by applying this method on the
retinal images. Fundus images of the retina were collected from a reputed eye clinic and 110 images were
trained and tested in order to extract the exudates and blood vessels. In this system we use the Probabilistic
Neural Network (PNN) for training and testing the pre-processed images. The results showed the retina is
normal or abnormal thereby analyzing the retinal image efficiently. There is 98% accuracy in the detection
of the exudates in the retina .
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.
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.
Resultados preliminares do implante de um novo anel associado ao PRK para pre...Ferrara Ophthalmics
Dr. Sandro Coscarelli, Dr. Pablo Rodrigues, Dr. Guilherme Rocha e Dr. Leonardo Torquetti compilaram e compartilham seus resultados com o uso de Segmentos de Anel de Ferrara HM associado ao PRK para a correção da miopia de pacientes com corneas finas e contra indicados para as técnicas de Excimer Laser apenas.
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).
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.
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.
DIAGNOSTICS-IMPACT ON THE PREMIUM CHANNEL - Heidelberg EngineeringHealthegy
Presentation from OIS@ASCRS 2016 - Kester Nahen, PhD, Managing Director
Video Presentation:
https://www.youtube.com/watch?v=2MqL1-bz4JE&list=PL1dmdBNnPTZJBhQxPOp0vdNg3s3wtN2yw&index=26
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.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
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
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.
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.
DIAGNOSTICS-IMPACT ON THE PREMIUM CHANNEL - Heidelberg EngineeringHealthegy
Presentation from OIS@ASCRS 2016 - Kester Nahen, PhD, Managing Director
Video Presentation:
https://www.youtube.com/watch?v=2MqL1-bz4JE&list=PL1dmdBNnPTZJBhQxPOp0vdNg3s3wtN2yw&index=26
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.
A Novel Approach for Diabetic Retinopthy ClassificationIJERA Editor
Sustainable Diabetic Mellitus may lead to several complications towards patients. One of the complications is
diabetic retinopathy. Diabetic retinopathy is the type of complication towards the retinal and interferes with
patient’s sight. Medical examination toward patients with diabetic retinopathy is observed directly through
retinal images using fundus camera. Diabetic retinopathy is classified into four classes based on severity, which
are: normal, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and
macular edema (ME). The aim of this research is to develop a method which can be used to classify the level of
severity of diabetic retinopathy based on patient’s retinal images. Seven texture features were extracted from
retinal images using gray level co-occurence matrix three dimensional method (3D-GLCM). These features are
maximum probability, correlation, contrast, energy, homogeneity, and entropy; subsequently trained using
Levenberg-Marquardt Backpropagation Neural Network (LMBP). This study used 600 data of patient’s retinal
images, consist of 450 data retinal images for training and 150 data retinal images for testing. Based on the result
of this test, the method can classify the severity of diabetic retinopathy with sensitivity of 97.37%, specificity of
75% and accuracy of 91.67%
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
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.
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.
There are three major complications of diabetes which lead to blindness. They are retinopathy, cataracts, and glaucoma among which diabetic retinopathy is considered as the most serious complication affecting the blood vessels in the retina. Diabetic retinopathy (DR) occurs when tiny vessels swell and leak fluid or abnormal new blood vessels grow hampering normal vision.
Diabetic retinopathy is a widespread problem of visual impairment. The abnormalities like microaneurysms, hemorrhages and exudates are the key symptoms which play an important role in diagnosis of diabetic retinopathy. Early detection of these abnormalities may prevent the blurred vision or vision loss due to diabetic retinopathy. Basically exudates are lipid lesions able to be seen in optical images. Exudates are categorized into hard exudates and soft exudates based on its appearance. Hard exudates come out as intense yellow regions and soft exudates have fuzzy manifestations. Automatic detection of exudates may aid ophthalmologists in diagnosis of diabetic retinopathy and its early treatment. Fig. 1 shows the key symptoms of diabetic retinopathy.
Abstract:
A technique for exudate detectionin fundus image is been presented in this paper. Due to diabetic retinopathy an abnormality is caused known as exudates.The loss of vision can be prevented by detecting the exudates as early as possible. The work mainly aims at detecting exudates which is present in the green channel of the RGB image by applying few preprocessing steps, DWT and feature extraction. The extracted features are fed to 3 different classifiers such as KNN, SVM and NN. Based on the classifier result if an exudate is present the extraction of exudate ROI is done based on canny edge detection followed by morphological operations. The severity of the exudates is established on the area of the detected exudate.
Keywords:Exudates, Fundus image, Diabetic retinopathy, DWT, KNN, SVM, NN, Canny edge detection, Morphological operations.
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,
which is the severe stage of Diabetic Retinopathy is performed using Morphological Operation. Using a clinical reference standard, images with exudates were detected with 96% success rate. This method appears promising as it can detect the very small areas of exudates.
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.
Classification of OCT Images for Detecting Diabetic Retinopathy Disease using...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
CLASSIFICATION OF OCT IMAGES FOR DETECTING DIABETIC RETINOPATHY DISEASE USING...sipij
Optical Coherence Tomography (OCT) imaging aids in retinal abnormality detection by showing the
tomographic retinal layers. OCT images are a useful tool for detecting Diabetic Retinopathy (DR) disease
because of their capability to capture micrometer-resolution. An automated technique was introduced to
differentiate DR images from normal ones. 214 images were subjected to the experiment, of which 160
images were used for classifiers’ training, and 54 images were used for testing. Different features were
extracted to feed our classifiers, including statistical features and local binary pattern (LBP) features. The
experimental results demonstrated that our classifiers were able to discriminate DR retina from the normal
retina with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 100%. The retinal
OCT images have common texture patterns and using a powerful tool for pattern analysis like LBP
features has a significant impact on the achieved results. The result has better performance than previously
proposed methods in the literature.
Sarah Ali Hasan, Dr. Mandeep Singh, “Automatic Diagnosis of Astigmatism for Pentacam Sagittal Maps”, Third International Workshop on Recent Advances in Medical Informatics (RAMI-2014) (Accepted to be published on (27-29Sep). IEEE conference.
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.
An effective deep learning network for detecting and classifying glaucomatous...IJECEIAES
Glaucoma is a well-known complex disease of the optic nerve that gradually damages eyesight due to the increase of intraocular pressure inside the eyes. Among two types of glaucoma, open-angle glaucoma is mostly happened by high intraocular pressure and can damage the eyes temporarily or sometimes permanently, another one is angle-closure glaucoma. Therefore, being diagnosed in the early stage is necessary to safe our vision. There are several ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy but require time and expertise. Using deep learning approaches could be a better solution. This study focused on the recognition of open-angle affected eyes from the fundus images using deep learning techniques. The study evolved by applying VGG16, VGG19, and ResNet50 deep neural network architectures for classifying glaucoma positive and negative eyes. The experiment was executed on a public dataset collected from Kaggle; however, every model performed better after augmenting the dataset, and the accuracy was between 93% and 97.56%. Among the three models, VGG19 achieved the highest accuracy at 97.56%.
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
Automated Detection of Microaneurysm, Hard Exudates, and Cotton Wool Spots in...iosrjce
The The automatic identification of Image processing techniques for abnormalities in retinal images.
Its very importance in diabetic retinopathy screening. Manual annotations of retinal images are rare and
exclusive to obtain. The ophthalmoscope used direct analysis is a small and portable apparatus contained of a
light source and a set of lenses view the retina. The existence of diabetic retinopathy detected can be examining
the retina for its individual features. The first presence of diabetic retinopathy is the form of Microaneurysms.
This paper describes different works needed to the automatic identification of hard exudates and cotton wool
spots in retinal images for diabetic retinopathy detection and support vector machine (SVM) for classifying
images. This system is evaluated on a large dataset containing 130 retinal images. The proposed method Results
show that exudates were detected from a database with 96.9% sensitivity, specificity 96.1% and
97.38%accuracy
SOLIX Essential is a technology built upon a proven foundation of high-speed Spectral Domain OCT. The SOLIX Essential offers state-of-the-art imaging from the cornea to the choroid with exclusive technology that will change your approach to disease diagnosis and management.
Similar to Glaucoma Detection and Image Processing Approaches: A Review (20)
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
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Final project report on grocery store management system..pdfKamal Acharya
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Cosmetic shop management system project report.pdf
Glaucoma Detection and Image Processing Approaches: A Review
1. 36
1
Medical Professional, 2
Associate Professor,
3, 4, 5
Scholars
1
Department of Surgery and 2
Department of Computer
Science, University of Cambridge, United Kingdom
3,4,5
Malco Vidyalaya Matriculation Higher Secondary School,
Mettur Dam, Salem, Tamil Nadu, India
Corresponding Author’s E-mail: psjkumar.@cam.ac.uk
Glaucoma Detection and Image Processing Approaches: A Review
1
J.Ruby,
2
P.S.Jagadeesh Kumar,
3
J.Lepika,
4
J.Tisa,
5
J.Nedumaan
ABSTRACT
Instinctive analysis of retina images is becoming an important
screening tool nowadays. This technique helps to detect
various kinds of risks and diseases of eyes. One of the most
common diseases which cause blindness is Glaucoma. This
disease happens due to the increase in Intraocular Pressure.
Early detection of this disease is indispensible to prevent the
interminable blindness. Screenings of glaucoma based on
digital images of the retina have been performed in the past
few years. Several procedures are there to distinguish the
abnormality of retina due to glaucoma. The significant image
processing techniques are image registration, image fusion,
image segmentation, feature extraction, image enhancement,
morphology, pattern matching, image classification, analysis,
and statistical measurements. The foremost idea behind this
paper is to describe a system that is mainly based on image
processing and classification techniques for detection of
glaucoma by comparing and measuring different parameters
of fundus images of glaucoma patients and normal patients.
Keywords: Glaucoma Identification, Image Analysis, Image
Segmentation, Machine Learning Algorithms, Computerized
Therapy, Retina, Automation, Intraocular Pressure.
How to cite this article: J.Ruby, P.S.Jagadeesh Kumar,
J.Lepika, J.Tisa, and J.Nedumaan. Glaucoma Detection and
Image Processing Approaches: A Review. J Current Glau
Prac 2014;8(1):36-41.
Source of support: Nil
Conflict of interest: None
INTRODUCTION
Glaucoma is a disease of the optic nerve involving
loss of retinal ganglion cells in a characteristic pattern of
optic neuropathy. Our eye has pressure just like our blood
which is called intraocular pressure. When this intraocular
pressure (IOP) increases to a certain level, it damages the
optical nerve. This can result in decreased peripheral
vision and eventually blindness. Manual analysis of eye
images is fairly time consuming and the accuracy of
parameters measurements varies between experts. Hence,
there arises the need for an automated technique.
Automatic Computerized examination of retina pictures is
turning into a significant screening instrument now days.
This strategy identifies different sort of dangers and
ailments of eyes. There are essentially two kinds of
glaucoma one is open point or interminable glaucoma and
another is shut edge or intense glaucoma, both are
answerable for expanding the intraocular pressure. In
beginning time of glaucoma, Patients don't for the most
part have any visual signs or indications. As the illness
progress it reasons for losing the vision and the patients
may experience the ill effects of limited focus (being just
ready to see halfway). Thusly early identification of this
malady is basic to anticipate the changeless visual
deficiency. There are a few computerized glaucoma
discovery strategies effectively accessible. In this
investigation, we are attempting to do a survey of each
one of those accessible systems. Every one of the systems
has a few favorable circumstances just as burdens. In view
of this investigation, we can figure out which method can
be applied in which situation to get the ideal outcome.
Automated Glaucoma Detection Techniques
Luntz MH, Rosenblatt M1
had concocted a technique
for automated detection of glaucoma in eye by using
angel open distances 500 calculations. This technique is a
three step methodology. In the first step, three features of
the ultrasound images i.e. contrast, resolution and clarity
is significantly improved for effective anterior chamber
segmentation. In second step, classification of several
regions of ultra sound image is done to eliminate the
unwanted image. Then they crop the anterior chamber
region and the reference axis is located. Step three focuses
on several involvements in exact determination of the
anterior chamber angle in finding out whether the eye is
affected by glaucoma or not. This algorithm is able to
correctly diagnose glaucoma in 97% of the cases.
Harbour JW, Rubsamen PE, Palmberg P2
devised a
new methodology for the detection of glaucoma based on
two vital features CDR and ISNT ratio. K-means
clustering is recursively applied to ROI to localize the
optic disc and optic cup region. An elliptical fitting
technique is used to calculate the CDR values. The blood
vessels inside the optic disc are extracted by local entropy
thresholding and four different masks are used to
determine the ISNT ratio. CDR and ISNT ratio are
calculated. Then they carried out a performance of the
proposed algorithm on three different classifiers.
2. J.Ruby, P.S.Jagadeesh Kumar, J.Lepika, J.Tisa, J.Nedumaan
Lynch MG, Brown RH3
adopted a technique which
extracted ROI from retinal images. Optic disc
segmentation is performed on the extracted ROI in order
to detect the disc boundary using optimal color channel.
The disc boundary obtained from the above step may not
denote the exact shape of the disc as boundary can be
affected by a large number of blood vessels at the
entrance of the disc. Hence, optic disc boundary
smoothing is performed using ellipse fitting for capturing
near perfect shape of the disc. Optic cup segmentation is
also performed. After the cup boundary has been
detected, ellipse fitting is again employed to eliminate
some of the cup boundary’s sudden changes in curvature.
Ellipse fitting becomes especially useful when portions of
the blood vessels in the neuro-retinal rim are included
within the detected boundary. The CDR is inevitably
obtained based on the height of detected cup and disc. If
the cup to disc ratio exceeds 0.3 then it indicates the
abnormal condition that is the presence of glaucoma.
DiSclafani M, Liebmann JM, Ritch R4
proposed a
technique for Glaucoma diagnosis utilizing fundus images
of the eye and the optical coherence tomography (OCT).
The Retinal Nerve Fiber Layer (RNFL) can be broadly
classified into anterior boundary (top layer of RNFL), the
posterior boundaries (bottom layer of RNFL) and also the
distance in between the two boundaries. Glaucomatous
and Non-Glaucomatous classification was done based on
the thickness of the nerve fiber layer which is nearly 105
μm. This approach provided optical disk detection with
97.75% accuracy.
Greenfield DS, Tello C, Budenz DL, Liebmann JM,
Ritch R5
presented a method to calculate the CDR
automatically from fundus images. The image pre-
processing is the first step to localize the optic disc and
cup. The optic disc is extracted using an edge detection
approach and a variation level-set approach separately.
The optic cup is then segmented using a color component
analysis method and threshold level-set method. After
obtaining the contours, an ellipse fitting step is introduced
to sharpen the obtained results. Using 44 images, the
performance of this approach is evaluated using the
proximity of the calculated CDR to the manually graded
CDR. The results indicating that our approach provides
89% accuracy in glaucoma analysis.
Cashwell et al. 6
proposed an algorithm for glaucoma
detection. In this algorithm, segmentation and feature
extraction of an image is done using active contours.
Next, the initial point of interested feature is determined
accordingly. Then masking is done by cropping the region
of interest. Finally, calculation of the open angle of the
anterior chamber is conducted. If the open angle is found
to be greater than threshold angle, the eye is diagnosed
as normal eye. Otherwise, it is diagnosed to be diseased
eye. Epstein, DL7
presented a new Optic disc (OD)
detection technique which is a vital step for automated
diagnosis of Glaucoma. In this paper they developed a
new template-based approach for differentiating the OD
from digital retinal images. This approach uses
morphological, edge detection techniques followed by
the Circular Hough Transform which helps to get a
circular OD boundary approximation. A pixel located
within the OD is taken as initial information. Glaucoma
is identified by recognizing the changes in depth, shape
and color that it produces in the OD. Thus, this
technique used segmentation as well as analysis to
detect Glaucoma automatically.
Lieberman, MF, Lee, DA8
adopted a technique for
automated Glaucoma diagnosis. In this technique, optic
disk identification is performed on retinal images. Next,
thresholding is done to find out the threshold value.
After that image segmentation is performed using k-
means clustering and Gabor wavelet transform. Then
optic disc and cup boundary smoothing is performed
using different morphological features. From this CDR
can be calculated. If the CDR ratio exceeds 0.3 it
indicates high Glaucoma for the tested patient.
Trope GE, Pavlin CJ, Bau A, Baumal CR, Foster
FS9
devised a novel automated Glaucoma detection
system. The proposed system deals with the Image
obtained from Stratus Anterior Segment Optical
Coherence Tomography (AS-OCT). The stratus Anterior
Optical Coherence Tomography (AS-OCT) images with
these diseases are detected and classified from the
normal images using the proposed fuzzy min-max
neural network based on Data-Core (DCFMN). Data-
core fuzzy min-max neural network (DCFMN) depicts
strong robustness as well as high accuracy in
classification. DCFMN contains two classes of neurons:
classifying neurons (CNs) and overlapping neurons
(OLNs). CNs is used to classify the patterns of data. The
OLN can handle all kinds of overlap in different hyper
boxes. A new type of membership function considering
the characteristics of data and the influence of noise is
designed for CNs in the DCFMN. The membership
function of Overlapping Neurons (OLNs) deals with the
relative position of data in the hyper boxes. This
algorithm is performed on a batch of 39 anterior
segment- Optical Coherence Tomography (AS-OCT)
images. The performance of the proposed system is
excellent and a classification rate of 97% is achieved.
Chandler PA et al.10
proposed a system where the main
feature which is been considered here for identifying the
37
3. JOCGP
vision impaired disease glaucoma is the cup-to-disk ratio
(CDR), which specifies the change in the cup area.
Increase intra ocular pressure (IOP) results in increase in
the area of the cup and this result in dramatic visual loss.
In this paper increase in cup area is analyzed by examining
the CDR value. The CDR was calculated by taking the
ratio between the area of optic cup and disc. CDR > 0.3
indicates glaucoma and CDR ≤ 0.3, is considered as
normal image. We examine the mean square error (MSE),
pixel signal to noise ratio (PSNR) and signal to noise ratio
(SNR) to quantify the performance of the above pre-
processing algorithms. The algorithm for the earlier
identification of Glaucoma by estimating CDR was
developed in this paper. The optic disc was segmented
using the three methods namely edge detection method,
optimal thresholding method and manual threshold
analysis are proposed in the paper. For the cup, threshold
level-set method is evaluated. The performance of various
methods was evaluated by comparing the CDR. It was
found that the manual threshold method and edge
detection method provides better estimation of CDR. The
method has been applied to nearly forty images and the
CDR was correctly identified.
Sharma A, Sii F, Shah P, Kirkby GR11
devised a
technique for glaucoma detection where optic disc
segmentation via pyramidal decomposition is carried out
on the retinal images which gives a better performance
than other algorithms. It is important to note that although
Pyramidal decomposition method with the help of Hough
transform is guaranteed to converge though it is very
sensitive to noise. So, multiple initializations are being
used to yield a better performance. Finally, they have
proposed a model approach using discriminant analysis
which has shown an improvement over the rest.
Stumpf TH, Austin M, Bloom PA, McNaught A,
Morgan JE12
developed an automated glaucoma detection
system having six different stages. The system is
comprised of six different stages: Preprocessing, Region
of Interest (ROI) Extraction, Feature Extraction stage,
Calculation of CDR, Classification and Performance
analysis stage. The system takes as input a fundus image.
In the preprocessing stage, illumination correction and
blood vessel removal takes place. After the analysis of
entire image, a small square having 360 X 360 pixels is
taken around the brightest region is denoted as ROI.
Feature extraction is done from the images. In this method
the features are extracted from optic disc and optic cup.
The diameter of cup and optic disc is used for calculating
Cup to disc ratio. It is extracted from the segmented optic
disc and cup. The classifiers viz. SVM, Back Propagation
Neural Network, ANFIS is used to differentiate between
normal and abnormal cases of glaucoma.
Glaucoma Detection and Image Processing Approaches
ANFIS, SVM and Back Propagation had achieved
accuracy of 97.77%, 98.12% and 97.35% respectively.
SivakCallcott JA, O’Day DM, Gass JD, Tsai JC13
devised a novel automated glaucoma classification
technique, depending on image features from fundus
photographs. In this study, data-driven technique does not
need any manual assistance. The system does not depend
on explicit structure segmentation and measurements.
First of all size differences, non-uniform illumination and
blood vessels are eliminated from the images. They then
extracted the high dimensional feature vectors. Finally
compression is done using PCA and the combination
before classification with SVMs takes place. The
Glaucoma Risk Index (GRI) produced by the proposed
system with a 2-stage SVM classification scheme
achieved 86% success rate. This is comparable to the
performance of medical experts in detecting glaucomatous
eyes from such images. Since GRI is computed
automatically from fundus images acquired by an
inexpensive and widely available camera it is suggested
that the system could be used in glaucoma mass
screenings. Tripathi RC, Li J, Tripathi BJ, Chalam KV,
Adamis AP14
developed a novel approach for glaucoma
detection. The main motive of this study is to map the
person’s eye color image with database of images
consisting of normal person as well as glaucoma affected
person. The different color variations inside the eye can be
compared by using images taken from high definition
laser camera. They are also known as fundus images. The
feature extraction of these fundus images may be carried
out using MATLAB software tool. By measuring the
color pixels in the affected area the observation shows that
the person is suffering from Glaucoma or not. To detect if
a person is suffering from Glaucoma or not, a test is
conducted using the image of a normal person which is
kept as reference and then several comparison are carried
out with the clinical observations of the person’s image.
Further if the result is positive (person is affected with
Glaucoma), also check for the three types of Glaucoma
Primary Angle Closure Glaucoma, Secondary Glaucoma,
and Congenital Glaucoma.
Allingham, RR. Karim, F. et al.15
anticipated a
technique for detection of glaucoma utilizing a vertical
cup-to-disc ratio. Finding the cup area is very difficult
task as the proposed method tries to measure the cup-to-
disc ratio using a vertical profile on the optic disc. After
that the blood vessels of the disc were removed from the
image. Then canny edge detection filter was used for
detection of the edge of optic disc. The edge of the cup
area on the vertical profile was calculated by the threshold
technique. Finally, the vertical cup-to-disc ratio was found
out as shown in Fig. 1A, 1B and 1C. In this study, they
Journal of Current Glaucoma Practice,January-April 2014; 8(1):36-41 38
4. J.Ruby, P.S.Jagadeesh Kumar, J.Lepika, J.Tisa, J.Nedumaan
Fig. 1A: Progressive iris atrophy
Fig. 1B: Chandler’s syndrome
Fig. 1C: Cogan-Reese syndrome
also presented a method for identifying glaucoma by C/D
ratio. The method correctly identified 80% of glaucoma
cases and 85% of normal cases. Browning DJ16
designed
automated glaucoma detection technique through medical
imaging informatics (AGLAIA-MII) that proceeds into
concern everything starting from patient personal data,
patient’s genome information for screening and medical
retinal fundus image. The AGLAIA-MII architecture uses
information from multiple sources, including subjects’
personal data, imaging information from retinal fundus
image, and patients’ genome information. Features from
each data source will be extracted automatically.
Subsequently, these features will be passed to a multiple
kernel learning (MKL) framework to generate a final
diagnosis outcome. AGLAIA-MII achieved an area under
curve value of 0.866, which is much better than 0.551,
0.722 and 0.810 obtained from the individual personal
data, image and genome information components,
respectively. This methodology had shown a substantial
improvement over the previous glaucoma detection
techniques depending on intraocular pressure.
Blinder et al.17
developed an automated classifier
based on adaptive neuro-fuzzy inference system (ANFIS)
which differentiate between normal and affected eyes.
Stratus optical coherence tomography (OCT) technique
was used for calculation of glaucoma variables (optic
nerve head topography, retinal nerve fiber layer
thickness). Decision making was performed in two stages:
feature extraction using the orthogonal array and the
selected variables were treated as the feeder to adaptive
neuro-fuzzy inference system (ANFIS), which was trained
with the back-propagation gradient descent method in
combination with the least squares method. With the
Stratus OCT parameters used as input, receiver operative
characteristic (ROC) curves were generated by ANFIS to
classify eyes as either glaucomatous or normal. The mean
deviation was -0.67 ± 0.62 dB in the normal group and -
5.87 ± 6.48 dB in the glaucoma group. The inferior
quadrant thickness parameter was used for distinguishing
between normal and glaucomatous eyes.
Peterson CB et al.18
presented a model for detecting
the change in images. These images were collected by
scanning laser polarimetry, for tracking the glaucomatous
progression. This methodology depends on image set of
23 healthy eyes and includes colored noise, incomplete
cornea and masking is done by the retinal blood vessels.
There are two more methodologies for tracking
progression by taking up one or two follow-up visits into
the account. Then they are tested on these simulated
images. Both of these methods are depending on Student's
tests, anisotropic filtering and morphological operations.
The images simulated by this technique are visually
pleasing and also show statistical properties to the real
images. This results in optimizing the detection methods.
The results reveal that tracking the progression depending
on two follow-up visits marks a great improvement in
sensitivity without affecting the specificity adversely.
39
5. JOCGP
Little HL et al.19 proposed a new technique for
glaucoma detection based on RetCam. In this study, for
glaucoma detection RetCam is used which is an imaging
modality that captures the image of irridocorneal angle.
The manual grading and analysis of the RetCam image is
quite a time consuming process. In this study, they
developed an intelligent system for analysis of
iridocorneal angle images, which can distinguish between
open angle glaucoma and closed angle glaucoma
automatically.
Flanagan DW, Blach RK20
developed an automated
OD parameterization technique depending on segmented
OD and cup regions which are collected from monocular
retinal images. An OD segmentation technique is
developed which works by integrating the information of
local images around each point of interest in
multidimensional feature space. This technique is quite
robust against any form of variations found in the OD
region. They utilized a cup segmentation technique
depending on anatomical information such as vessel
bends at the cup boundary, which is quite vital as
considered by glaucoma experts. In this study, a multi-
stage strategy is used to find a reliable subset of vessel
bends called r-bends, which is followed by a local spline
fitting in order to find the desired cup boundary.
Goodart R, Blankenship G 21
developed an
automated glaucoma detection system by combining the
texture and higher order spectra (HOS) features obtained
from fundus images. Naive Bayesian, Support vector
machine, random-forest classifiers and sequential minimal
optimization are used to perform supervised classification.
After z-score normalization and feature selection, the
results reveal that the texture and HOS based features.
When these features are combined with a random-forest
classifier it performs much better than the other
classifiers. This method correctly diagnoses the glaucoma
images with 91% accuracy.
Takihara Y et al.22
developed a new segmentation
algorithm, depending on the expectation-maximization.
This algorithm used an anisotropic Markov random field
(MRF). In this study, structure tensor had been used to
characterize the predominant structure direction as well as
spatial coherence at each point. This algorithm had been
tested on an artificial validation dataset that is similar to
ONH datasets. This algorithm provides an accurate,
spatially consistent segmentation of this structure.
Herschler J, Agness D23
adopted super-pixel classification
based disc and cup segmentation technique for glaucoma
screening. This paper is presented and evaluated for
Glaucoma as shown in Fig 2A and 2B in induced angle
closure and secondary angle closure of topiramate and its
Glaucoma Detection and Image Processing Approaches
detection in patients using multimodalities including CSS,
Contrast Enhanced Histograms, K- Means clustering,
SLIC and Gabor wavelet transformation of the color
fundus camera image to obtain accurate boundary
delineation. Using structural features like CDR (Cut to
Disc Ratio), eccentricity and compactness the ratio value
exceeds 0.6 shall be recommended for further analysis of
a patient to the ophthalmologist.
Mei-Ling Huang, Hsin-Yi Chen, Jian-Jun Huang24
developed a glaucoma screening technique using super
pixel classification on optic disc and optic cup
segmentation. In optic disc segmentation, histograms
were utilized to classify each super pixel as disc or non-
disc. The quality of the automated optic disc segmentation
is calculated using a self-assessment reliability score. For
optic cup segmentation, along with the histograms, the
location information is also included to boost up the
performance. In this proposed segmentation approach a
database of 650 images was used with optic disc and optic
cup boundaries which had been manually marked by
professionals. The results showed an overlapping error of
9.5% and 24.1% in disc and cup segmentation,
respectively.
Koen A et al.5
developed an automated glaucoma
classification system that does not at all depend on the
segmentation measurements. They had taken a purely
data-driven approach which is very useful in large-scale
screening. This algorithm undertakes a standard pattern
recognition approach with a 2-stage classification step. In
this study, various image- based features were analyzed
and integrated to capture glaucomatous structures. There
are certain disease independent variations such as size
differences, illumination in homogeneities and vessel
structures which are removed in the preprocessing phase.
This system got 86% success rate on a data set of 200 real
images of healthy and glaucomatous eyes.
Conclusion
In this survey paper, numerous works identified with
computerized glaucoma identification were considered.
Glaucoma is one of the crucial elements adding to the
majority of visual impairment all around. It is important to
build up some modest computerized procedures for the
exact discovery of various phases of glaucoma. These
systems will be of bizarre assistance in underdeveloped
nations where there is an intense lack of ophthalmologists.
In the future, it is expected to grow increasingly precise
with the goal that the advantages are passed on the least
fortunate of needy persons. When glaucoma is accurately
analyzed then they can take appropriate finding or
experienced medical procedures in a convenient way to
maintain a tactical distance from all out visual impairment.
Journal of Current Glaucoma Practice,January-April 2014;8(1):36-41 40
6. J.Ruby, P.S.Jagadeesh Kumar, J.Lepika, J.Tisa, J.Nedumaan
Figs 2A and B: (A) Shallow anterior chamber depth due to topiramate induced angle closure, (B) UBM showing supraciliary
effusion following topiramate intake causing secondary angle closure
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