This document presents a new method for glaucoma detection using cup-to-disc ratio (CDR) calculation on fundus eye images. The method uses fuzzy C-means clustering combined with thresholding to extract the optic disc and optic cup from color fundus images. It calculates the vertical CDR automatically. The method was tested on 365 fundus images from public databases and an ophthalmologist, showing promising results for clinical use in glaucoma screening.
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.
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.
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.
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
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.
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 .
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.
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.
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.
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
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.
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 .
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.
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.
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.
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.
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
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
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.
A Systematic Comparison of Spectral-Domain Optical Coherence Tomography and F...John Redaelli
Ophthalmology - Sept. 2011 - A Systematic Comparison of Spectral-Domain Optical Coherence Tomography and Fundus Autofluorescence in Patients with Geographic Atrophy
Vessels delineation in retinal images using COSFIRE filtersNicola Strisciuglio
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002
The source code of the B-COSFIRE filters is available at:
http://www.mathworks.com/matlabcentral/fileexchange/49172-trainable-cosfire-filters-for-vessel-delineation-with-application-to-retinal-images
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle
noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those
corrupted images. Till now many researchers worked to remove speckle noise using frequency domain
methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and
Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters
used to remove speckle noise.
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.
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.
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.
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.
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
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
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.
A Systematic Comparison of Spectral-Domain Optical Coherence Tomography and F...John Redaelli
Ophthalmology - Sept. 2011 - A Systematic Comparison of Spectral-Domain Optical Coherence Tomography and Fundus Autofluorescence in Patients with Geographic Atrophy
Vessels delineation in retinal images using COSFIRE filtersNicola Strisciuglio
George Azzopardi, Nicola Strisciuglio, Mario Vento, Nicolai Petkov - "Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002
The source code of the B-COSFIRE filters is available at:
http://www.mathworks.com/matlabcentral/fileexchange/49172-trainable-cosfire-filters-for-vessel-delineation-with-application-to-retinal-images
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle
noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those
corrupted images. Till now many researchers worked to remove speckle noise using frequency domain
methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and
Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters
used to remove speckle noise.
AN MINIMUM RECONFIGURATION PROBABILITY ROUTING ALGORITHM FOR RWA IN ALL-OPTIC...sipij
In this paper, we present a detailed study of Minimum Reconfiguration Probability Routing (MRPR) algorithm, and its performance evaluation in comparison with Adaptive unconstrained routing (AUR) and Least Loaded routing (LLR) algorithms. We have minimized the effects of failures on link and router failure in the network under changing load conditions, we assess the probability of service and number of light path failures due to link or route failure on Wavelength Interchange(WI) network. The computation complexity is reduced by using Kalman Filter(KF) techniques. The minimum reconfiguration probability
routing (MRPR) algorithm selects most reliable routes and assign wavelengths to connections in a manner that utilizes the light path(LP) established efficiently considering all possible requests.
A BINARY TO RESIDUE CONVERSION USING NEW PROPOSED NON-COPRIME MODULI SETsipij
Residue Number System is generally supposed to use co-prime moduli set. Non-coprime moduli sets are a
field in RNS which is little studied. That's why this work was devoted to them. The resources that discuss
non-coprime in RNS are very limited. For the previous reasons, this paper analyses the RNS conversion
using suggested non-coprime moduli set.
This paper suggests a new non-coprime moduli set and investigates its performance. The suggested new
moduli set has the general representation as {2n
–2, 2n
, 2n+2}, where n ∈ {2,3,…..,∞}. The calculations
among the moduli are done with this n value. These moduli are 2 spaces apart on the numbers line from
each other. This range helps in the algorithm’s calculations as to be shown.
The proposed non-coprime moduli set is investigated. Conversion algorithm from Binary to Residue is
developed. Correctness of the algorithm was obtained through simulation program. Conversion algorithm
is implemented.
An Innovative Moving Object Detection and Tracking System by Using Modified R...sipij
The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the
limitations which are existing nowadays. Although high performance ratio for video object detection and
tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to
propose a novel video object detection and tracking technique so as to minimize the computational
complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature
extraction, background subtraction and hole filling. Originally the video clip in the database is split into
frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this
stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified
region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract
the multi features from the segmented image and the background image, the feature value thus obtained
are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The
foreground image is then subjected to morphological operations of erosion and dilation so as to fill the
holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus
the moving object is tracked in this stage. This method will be employed in MATLAB platform and the
outcomes will be studied and compared with the existing techniques so as to reveal the performance of the
novel video object detection and tracking technique.
TARGET LOCALIZATION IN WIRELESS SENSOR NETWORKS BASED ON RECEIVED SIGNAL STRE...sipij
We consider the problem of localizing a target taking the help of a set of anchor beacon nodes. A small
number of beacon nodes are deployed at known locations in the area. The target can detect a beacon
provided it happens to lie within the beacon’s transmission range. Thus, the target obtains a measurement
vector containing the readings of the beacons: ‘1’ corresponding to a beacon if it is able to detect the
target, and ‘0’ if the beacon is not able to detect the target. The goal is twofold: to determine the location
of the target based on the binary measurement vector at the target; and to study the behaviour of the
localization uncertainty as a function of the beacon transmission range (sensing radius) and the number of
beacons deployed. Beacon transmission range means signal strength of the beacon to transmit and receive
the signals which is called as Received Signal Strength (RSS). To localize the target, we propose a gridmapping
based approach, where the readings corresponding to locations on a grid overlaid on the region
of interest are used to localize the target. To study the behaviour of the localization uncertainty as a
function of the sensing radius and number of beacons, extensive simulations and numerical experiments
are carried out. The results provide insights into the importance of optimally setting the sensing radius and
the improvement obtainable with increasing number of beacons.
Automatic meal inspection system using lbp hf feature for central kitchensipij
This paper proposes an intelligent and automatic meal inspection system which can be applied to the meal
inspection for the application of central kitchen automation. The diet specifically designed for the patients are required with providing personalized diet such as low sodium intake or some necessary food. Hence,
the proposed system can benefit the inspection process that is often performed manually. In the proposed
system, firstly, the meal box can be detected and located automatically with the vision-based method and
then all the food ingredients can be identified by using the color and LBP-HF texture features. Secondly,
the quantity for each of food ingredient is estimated by using the image depth information. The experimental results show that the meal inspection accuracy can approach 80%, meal inspection efficiency can reach1200ms, and the food quantity accuracy is about 90%. The proposed system is expected to increase the capacity of meal supply over 50% and be helpful to the dietician in the hospital for saving the time in the diet inspection process.
A R EVIEW P APER : N OISE M ODELS IN D IGITAL I MAGE P ROCESSINGsipij
Noise is always presents in digital images during
image acquisition, coding, transmission, and proces
sing
steps. Noise is very difficult to remove it from t
he digital images without the prior knowledge of no
ise
model. That is why, review of noise models are esse
ntial in the study of image denoising techniques. I
n this
paper, we express a brief overview of various noise
models. These noise models can be selected by anal
ysis
of their origin. In this way, we present a complete
and quantitative analysis of noise models availabl
e in
digital images.
MULTIPLE OBJECTS TRACKING IN SURVEILLANCE VIDEO USING COLOR AND HU MOMENTSsipij
Multiple objects tracking finds its applications in many high level vision analysis like object behaviour
interpretation and gait recognition. In this paper, a feature based method to track the multiple moving
objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu
moments features from the motion segmented object blob and establishing the association of objects in the
successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor
classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the
robustness of the proposed method. The proposed method is assessed quantitatively using the precision and
recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit
the efficacy of the proposed method.
Stem calyx recognition of an apple using shape descriptorssipij
This paper presents a novel method to recognize stem - calyx of an apple using shape descriptors. The main
drawback of existing apple grading techniques is that stem - calyx part of an apple is treated as defects,
this leads to poor grading of apples. In order to overcome this drawback, we proposed an approach to
recognize stem-calyx and differentiated from true defects based on shape features. Our method comprises
of steps such as segmentation of apple using grow-cut method, candidate objects such as stem-calyx and
small defects are detected using multi-threshold segmentation. The shape features are extracted from
detected objects using Multifractal, Fourier and Radon descriptor and finally stem-calyx regions are
recognized and differentiated from true defects using SVM classifier. The proposed algorithm is evaluated
using experiments conducted on apple image dataset and results exhibit considerable improvement in
recognition of stem-calyx region compared to other techniques.
A Hybrid Architecture for Tracking People in Real-Time Using a Video Surveill...sipij
This paper describes a novel method for tracking customers using images taken from video-surveillance
cameras. This system analyzes the number of customers and their motions through the aisles of big-box
stores (supermarkets) in real-time. The originality of our approach is based on the study of the blobs
properties for managing the splitting/merging issues using a mathematical morphology operator. In the
order hand, in order to manage a high number of customers in real-time, we combine the advantage of two
tracking algorithms.
A FLUXGATE SENSOR APPLICATION: COIN IDENTIFICATIONsipij
Today, coins are used to operate many electric devices that are open to the public service. Washing machines, play stations, computers, auto brooms, foam machines, beverage machines, telephone chargers, hair dryers and water heaters are some examples of these devices These devices include coin recognition systems. In these systems, there are coils at two different radius, which become electromagnets when the
current is passed through them. The AC current supplied to the coils creates a variable magnetic field, which induces the eddy current on the coil during the passing of money. The magnetic field generated by the Eddy current reduces the current passing through the coil. The amount of change of current in the coil gives information about the coin; the type of metal (element) and the amount of metal (element). In this
study, a new coin identification system (magnetic measurement system) is designed. In this system, the
magnetic anomaly generated by the coin as a result of applying direct current to the coils is tried to be
detected by fluxgate sensor. In this study, sensor voltages are acquired in computer environment by using
developed electronic unit and LabVIEW based software. In the paper, experimental results have been
discussed in detail.
Optimized Biometric System Based on Combination of Face Images and Log Transf...sipij
The biometrics are used to identify a person effectively. In this paper, we propose optimised Face
recognition system based on log transformation and combination of face image features vectors. The face
images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation
is applied on enhanced image to generate features. The feature vectors of many images of a single person
image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is
used to compare test image feature vector with database feature vectors to identify a person. It is
experimented that, the performance of proposed algorithm is better compared to existing algorithms.
The paper addresses the automation of the task of an epigraphist in reading and deciphering inscriptions.
The automation steps include Pre-processing, Segmentation, Feature Extraction and Recognition. Preprocessing
involves, enhancement of degraded ancient document images which is achieved through Spatial
filtering methods, followed by binarization of the enhanced image. Segmentation is carried out using Drop
Fall and Water Reservoir approaches, to obtain sampled characters. Next Gabor and Zonal features are
extracted for the sampled characters, and stored as feature vectors for training. Artificial Neural Network
(ANN) is trained with these feature vectors and later used for classification of new test characters. Finally
the classified characters are mapped to characters of modern form. The system showed good results when
tested on the nearly 150 samples of ancient Kannada epigraphs from Ashoka and Hoysala periods. An
average Recognition accuracy of 80.2% for Ashoka period and 75.6% for Hoysala period is achieved.
Efficient pu mode decision and motion estimation for h.264 avc to hevc transc...sipij
H.264/AVC has been widely applied to various applications. However, a new video compression standard,
High Efficient Video Coding (HEVC), had been finalized in 2013. In this work, a fast transcoder from
H.264/AVC to HEVC is proposed. The proposed algorithm includes the fast prediction unit (PU) decision
and the fast motion estimation. With the strong relation between H.264/AVC and HEVC, the modes,
residuals, and variance of motion vectors (MVs) extracted from H.264/AVC can be reused to predict the
current encoding PU of HEVC. Furthermore, the MVs from H.264/AVC are used to decide the search
range of PU during motion estimation. Simulation results show that the proposed algorithm can save up to
53% of the encoding time and maintains the rate-distortion (R-D) performance for HEVC.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
INHIBITION AND SET-SHIFTING TASKS IN CENTRAL EXECUTIVE FUNCTION OF WORKING ME...sipij
Understanding of neuro-dynamics of a complex higher cognitive process, Working Memory (WM) is
challenging. In WM, information processing occurs through four subsystems: phonological loop, visual
sketch pad, memory buffer and central executive function (CEF). CEF plays a principal role in WM. In this
study, our objective was to understand the neurospatial correlates of CEF during inhibition and set-shifting
processes. Thirty healthy educated subjects were selected. Event-Related Potential (ERP) related to visual
inhibition and set-shifting task was collected using 32 channel EEG system. Activation of those ERPs
components was analyzed using amplitudes of positive and negative peaks. Experiment was controlled
using certain parametric constraints to judge behavior, based on average responses in order to establish
relationship between ERP and local area of brain activation and represented using standardized low
resolution brain electromagnetic tomography. The average score of correct responses was higher for
inhibition task (87.5%) as compared to set-shifting task (59.5%). The peak amplitude of neuronal activity
for inhibition task was lower compared to set-shifting task in fronto-parieto-central regions. Hence this
proposed paradigm and technique can be used to measure inhibition and set-shifting neuronal processes in
understanding pathological central executive functioning in patients with neuro-psychiatric disorders.
DETERMINATION OF BURIED MAGNETIC MATERIAL’S GEOMETRIC DIMENSIONSsipij
It is important to find buried magnetic material’s geometric features that are parallel to the soil surface in
order to determine anti-tank and anti-personnel mine compatible to standards. So that it is possible to
decrease the number of false alarms by separating the samples that have got non-standard geometries. For
this purpose, in this study the anomalies occurred at horizontal component of the earth’s magnetic field by
buried samples are determined with magnetic sensor. In the study, KMZ51 AMR is used as the magnetic
sensor. The position-controlled movement of the sensor along x-y axis is provided with 2D scanning system.
Trigger values of sensor output are evaluated with respect to the scanning field. The experiments are
redone for the samples at different geometries and variables are defined for geometric analysis. The
experimental conclusions obtained from this paper will be discussed in detail.
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.
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.
Segmentation of Blood Vessels and Optic Disc in Retinal Imagesresearchinventy
Retinal image analysis is increasingly prominent as a non-intrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disc in the fundus retinal images. The method could be used to support non-intrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disc is an important indicator for diseases like diabetic retinopathy, glaucoma and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disc. The optic disc segmentation is performed using two alternative methods. The Markov Random Field (MRF) image reconstruction method segments the optic disc by removing vessels from the optic disc region and the Compensation Factor method segments the optic disc using prior local intensity knowledge of the vessels. The proposed method is tested on three public data sets, DIARETDB1, DRIVE and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disc.
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.
Glaucoma Disease Diagnosis Using Feed Forward Neural Network ijcisjournal
Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to
complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure
(IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible
blindness, so it should be diagnosed and treated properly at an early stage. In this paper,
thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for
obtaining average and energy texture feature which are used to classify glaucoma disease with high
accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%.
In this work, the computational complexity is minimized by reducing the number of filters while retaining
the same accuracy.
C LASSIFICATION O F D IABETES R ETINA I MAGES U SING B LOOD V ESSEL A REASIJCI JOURNAL
Retina images are obtained from the fundus camera a
nd graded by skilled professionals. However there i
s
considerable shortage of expert observers has encou
raged computer assisted monitoring. Evaluation of
blood vessels network plays an important task in a
variety of medical diagnosis. Manifestations of
numerous vascular disorders, such as diabetic retin
opathy, depend on detection of the blood vessels
network. In this work the fundus RGB image is used
for obtaining the traces of blood vessels and areas
of
blood vessels are used for detection of Diabetic Re
tinopathy (DR). The algorithm developed uses
morphological operation to extract blood vessels. M
ainly two steps are used: firstly enhancement opera
tion
is applied to original retina image to remove noise
and increase contrast of retinal blood vessels. Se
condly
morphology operations are used to take out blood ve
ssels. Experiments are conducted on publicly availa
ble
DIARETDB1 database. Experimental results obtained b
y using gray-scale images have been presented.
Automated Detection of Optic Disc in Retinal FundusImages Using PCAiosrjce
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.
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FUZZY CLUSTERING BASED GLAUCOMA DETECTION USING THE CDR
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.3, June 2015
DOI : 10.5121/sipij.2015.6305 55
FUZZY CLUSTERING BASED GLAUCOMA
DETECTION USING THE CDR
Thresiamma Devasia1
, Poulose Jacob2
and Tessamma Thomas3
1
Department of Computer Science,
Assumption College Changanacherry, Kerala, India
2
Department of Computer Science,
Cochin University of Science and Technology, Kerala, India
3
Department of Electronics, Cochin University of Science and Technology, Kerala, India
ABSTRACT
Glaucoma is a serious eye disease, overtime it will result in gradual blindness. Early detection of the
disease will help prevent against developing a more serious condition. A vertical cup-to-disc ratio which is
the 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.
KEYWORDS
fundus image, optic disc, optic cup, Cup-to-Disc Ratio , Fuzzy C Means Clustering
1. INTRODUCTION
Glaucoma is a disease of increased pressure within the eyeball. The disease is mostly caused due
to increased intraocular pressure (IOP) resulting from a malfunction or malformation of the eye’s
drainage structures. If left untreated, it would lead to degeneration of optic nerve and retinal
fibers. Early diagnosis of glaucoma through analysis of the neuro-retinal optic disc (OD) and
optic cup (OC) area is crucial. The increase in pressure results in immoderate amount of stress to
be put to the attachment of the optic nerve to the eye. Lack of treatment for glaucoma can lead to
permanent blindness. Early detection of the disease will help prevent against developing a more
serious condition. The fundus images are used for diagnosis by trained clinicians to check for any
abnormalities or any change in the retina. Important anatomical structures captured in a fundus
image are blood vessels, OD, OC, and macula for a normal retina. An image of a diseased retina
may also contain many visible symptoms of the eye-disease. In a healthy retinal image the OD
usually appears as a circular shaped bright yellowish object which is partly covered with vessels.
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.3, June 2015
56
The OC is the cupping of the optic nerve and that means the size of the depression in the middle
of the nerve when viewed from the front of the eye. When there is damage to the optic nerve, the
cupping increases. Changes in the OD and OC can indicate the presence, current state and
progression of glaucoma [1][2].
Since the colour fundus images provide early signs of certain diseases such as diabetes, glaucoma
etc., colour fundus images are used to track the eye diseases by the ophthalmologists. Figure1
shows the important features of a retinal colour fundus image.
Figure1. Colour Fundus Image
Diseases with symptoms on the fundus images are very complex. Several main symptoms of
diseases expressed on the disk optic, blood vessels, haemorrhage and lesion areas on the retinal
background. For the OD, differences in the color, shape, edge or vasculature may signify a
pathological change or may just be part of the wide spectrum of normality. The physiological cup
varies in shape, size and depth, and any enlargement of the cup is one parameter used in the
assessment of glaucoma. Figure 2 shows the colour fundus image of a normal eye and
glaucomatous eye.
(i) (ii)
Figure 2. (i) Fundus image of a normal eye (ii) Glaucomatous eye fundus image
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.3, June 2015
57
One of the hallmarks of glaucoma is the optic nerve damage, which is characterized by cupping
of the optic nerve. Even a normal optic nerve has a small amount of cupping. However, the
patients with glaucoma tend to have larger cupping than normal subjects. The cup-to-disc ratio
(CDR) of normal subjects is typically around 0.2 to 0.4 as it is shown in Figure 2(i). However,
with glaucoma, there is progressive loss of optic nerve fibers, and consequent increase in the cup
size of the optic nerve[3]. Figure 2(ii) shows a glaucomatous fundus image. Nowadays, CDR is
manually determined by trained ophthalmologists and has to be diagnosed by using retinal
images from special cameras which are expensive. These are limitations of potential in mass
screening for early detection. This paper focused on developing a new method that can provide
suitable way to compute CDR result which give the most accurate result and prevent from current
limitation.
This paper is organized as follows:
Section 2 presents a brief survey of existing literature. Section 3 describes the materials used for
the present work. A new algorithm to efficiently extract OD and OC in ocular fundus images and
computation of CDR are given in Section 4. The results are presented in Section 5, and
Conclusions are given in the final Section 6.
2. LITERATURE SURVEY
Different techniques are described in the literature for OD and OC extraction for the computation
of the CDR. The Active Shape Model (ASM) based optical disk detection is implemented by
Huiqi et al.[4]. The initialization of the parameters for this model is based on Principal
Component Analysis technique. The faster convergence rate and the robustness of the technique
are proved by experimental results. Huajun Ying et al. [5] designed a fractal-based automatic
localization and segmentation of optic disc in retinal images. K. Shekar [6] developed a method
for OD segmentation using Hussain, A.R. et al. [7] proposed a method for optic nerve head
segmentation using genetic active contours. J.Liu et al. proposed a technique to extract optic disc
and cup in order to determine cup to disc ratio. Optic disc is extracted using a variational level set
method and the detected contour is uneven due to influence of blood vessels. Detection of cup
boundary was performed using intensity and threshold level set approach. Thresholding
techniques produced better results for both high and low risk retinal images. An ellipse fitting is
used to smoothen the boundary [8,9].
Zhuo Zhang et al. [10] designed a convex hull based neuro-retinal optic cup ellipse optimization
technique. Wong, D.W.K. et al. [11] developed SVM-based model optic cup detection for
glaucoma detection using the cup to disc ratio in retinal fundus images. Joshi G.D. et al. [12]
developed vessel bend-based cup segmentation in retinal images. Shijian Lu et al. [13] proposed
a background elimination method for the automatic detection of OD. Gopal Datt Joshi developed
a deformable model guided by regional statistics to detect the OD boundary. Cup boundary
detection scheme is based on Lab color space and the expected cup symmetry. This method uses
sector wise information and give rise to fewer false positives and hence better specificity. Error
value computed is less for a normal image than for a glaucomatous image [14]. Morphological
operations were used for locating the optic disc in retinal images by Angel Suero et al. [15].
In this paper, a new algorithm based on Fuzzy C-Means Clustering (FCM) technique combined
with thresholding, is used for OD and OC extraction. This new method, firstly, extracts the OD
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and OC of the colour fundus image and computes the vertical CDR automatically. This is an
efficient method for the automatic screening of colour fundus image for CDR computation.
3. MATERIALS AND METHODS
The fundus images used in these experiments are taken from publicly available databases DRION
and DIARATDB0 and, images from Giridhar Eye Institute, Kochi, Kerala. The CDRs obtained
from an ophthalmologist is used as ground truth for the evaluation.
4. DEVELOPED ALGORITHM
The new approach is composed of four steps. The channels of the colour retinal are separated.
The blood vessels are removed, applying the contrast adjustment to enhance the low contrast
image image. The Fuzzy C Means combined with thresholding is applied on the red channel of
the input image for the extraction of the OD and the same technique is applied on the green
channel of the input image for the extraction of OC. The CDR is computed using the ratio of
vertical diameter of OC and OD.
4.1. Preprocessing
The preprocessing step excludes variations due to image acquisition, such as inhomogeneous
illumination. In preprocessing, techniques such as morphological operations and contrast
enhancement are applied on the input image. The following sections include different
preprocessing operations used in this paper.
4.1.1. Preprocessing steps for Optic Disc Extraction
4.1.1.1. Selection of Red Channel
From the previous studies it is shown that even though the green component of an RGB
retinography is the one with highest contrast, the OD is often present in the red field as a well-
defined white shape, brighter than the surrounding area [16]. Therefore the red channel of the
RGB colour images is used for the extraction of OD regions in the retinal fundus images.
4.1.1.2. Removal of Blood Vessels
Since blood vessels within the OD are strong distracters, they should be erased from the image
beforehand. In this method a morphological closing operation is performed on the red channel.
The dilation operation first removes the blood vessels and then the erosion operation
approximately restores the boundaries to their former position.
Closing : (1)
where A is the red channel of the input image and B is a 10x10 symmetrical disc structuring
element, to remove the blood vessels[17,18]. C is the resultant vessel free, smoothed output
image.
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4.1.2. Preprocessing steps for Optic Cup Extraction
4.1.2.1. Selection of Green Channel
The green channel has low contrast variation which gives more differentiation between the blood
vessel and OC. The green channel, therefore, is selected for the extraction of the OC of the retinal
image.
4.1.2.2. Removal of Blood Vessels
Blood vessels in the green channel were removed using a morphological closing procedure,
(2)
where I is the green channel of the input image and B is an 8x8 symmetrical disc structuring
element, to remove the blood vessels[17,18]. I2 is the smoothed, vessel free output image. Figure
3 shows the preprocessing operations on the input image.
Figure 3.The preprocessing steps (a) Input Image (b) Red channel (c ) Blood vessel free image (d) Green
channel (e) Blood vessel free image
4.2. Feature Extraction
Medical image segmentation is a difficult task due to the complexity of segmentation. Because of
its simplicity and efficiency, threshold segmentation is wildly used in many fields. Assessment of
OD and OC is important in discriminating between normal and pathological retinal images. The
OD is a bright pattern of the fundus image. Recently, many studies on the use of fundus images
in extracting OD and OC have been reported. FCM clustering with thresholding is used in this
work for the extraction of OD and OC.
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4.3. Fuzzy C Means Clustering with Thresholding
The new method is a combination of fuzzy algorithm, C Means clustering and thresholding.
Clustering involves the task of dividing data points into homogeneous classes or clusters so that
items in the same class are as similar as possible and items in different classes are as dissimilar as
possible. Clustering can also be thought of as a form of data compression, where a large number
of samples are converted into a small number of representative prototypes or clusters. Different
types of similarity measures may be used to identify classes depending on the data and the
application, where the similarity measure controls the formation of the clusters. In the following
new method intensity value is used as the similarity measure. Thresholding is one of the most
powerful techniques for image segmentation, in which the pixels are partitioned depending on
their intensity value.
4.3.1. Fuzzy C-Means Clustering Algorithm
FCM clustering is a clustering technique and it employs fuzzy partitioning such that a data point
can belong to all groups with different membership grades between 0 and 1. It is an iterative
algorithm. The aim of FCM is to find cluster centers (centroids) that minimize a dissimilarity
function. Corresponding to each cluster center, this algorithm works by assigning membership to
each data point on the basis of the difference between the cluster center and the data point. The
more the data is near to the cluster center, the more is its membership towards the particular
cluster center. It is obvious that the summation of membership of each data point should be equal
to one.
(3)
(4)
where Xkj is data element, dik is the distance matrix and Vij is the element of the cluster center
vector.
The dissimilarity function which is used in FCM is given Equation (5)
(5)
uij is between 0 and 1;
ci is the centroid of cluster i;
dij is the Euclidian distance between ith
centroid (ci) and jth
data point;
m є [1,∞] is a weighting exponent.
To reach a minimum of dissimilarity function there are two conditions. These are given in
Equation (6) and Equation (7)
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(6)
(7)
This algorithm determines the following steps [4].
Step1. Randomly initialize the membership matrix (U) that has constraints in Equation 7.
Step2. Calculate centroids (Ci) by using Equation (6).
Step3. Compute dissimilarity between centroids and data points using equation (5). Stop if its
improvement over previous iteration is below a threshold.
Step4. Compute a new U using Equation (7). Go to Step 2 [19][20].
By iteratively updating the cluster centers and the membership grades for each data point, FCM
iteratively moves the cluster centers to the apt location within a data set. To accommodate the
introduction of fuzzy partitioning, the membership matrix (U) is randomly initialized according
to Equation (7).
The Fuzzy Logic Toolbox command line function fcm is used for generating clusters, and in this
paper three clusters are generated from the vessel free enhanced image. The fcm function
iteratively moves the cluster centers to the right location within the data set. The outputs are 3
cluster centers C1, C2 and C3 and membership function matrix M with membership-grades,
which is the intensity value of each pixel.
4.4. Thresholding
Thresholding is the operation of converting a multilevel image into a binary image i.e., it assigns
the value of 0 (background) or 1 (objects or foreground) to each pixel of an image based on a
comparison with some threshold value T (intensity or colour value) [17][18]. By applying the
threshold T on an image, the image is converted to a binary image. The following formula (8)
[13] is used for the binary image extraction.
(8)
where I is the input image, T is the threshold and IT is the binary image after thresholding.
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4.5. Extraction of Optic Disc
The main feature of the OD is that it is having the highest intensity. Therefore the highest
intensity is used as the threshold for the OD extraction. The threshold T is computed using the
following method. From the generated clusters, first the cluster with maximum membership
grade is found, and the corresponding grades are assigned with the same identification label.
From the smoothed image, pixels with this gray level value are accessed, the average of the
maximum and minimum intensity values are computed to obtain the threshold value T1.
(9)
In the above equation, data represents the data points of the smoothed red channel image and
label represents the cluster value with the highest membership grade. By applying the threshold
T1 on the smoothed image IS the image is converted to a binary image. The formula (9) is used
for the binary image extraction.
Figure 4. The extracted binary image
Since the OD is of circular shape, the OD region selection process needs to be made specific to
the circular region. So the largest connected component Ri whose shape is approximately circular
is selected using the compactness measure
(10)
where, P(Ri) is the perimeter of the region Ri and A(Ri) is the area of the region Ri. The binary
image with the compactness smaller than the pre-specified value, (5 in the present study) is
considered as the optic disc approximation. Thus using the condition C < 5, extraction of round
objects is done, eliminating those objects that do not meet the criteria. In some cases the extracted
image contains small unwanted objects. The erosion operation is used to remove these objects
[21].
Figure 5 Extracted Optic Disc
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The mean of the rows and columns form the centroid (Y1, X1) of the OD.
(11)
(12)
where m is the number of rows and n is the number of columns.
From the above coordinates of the optic disc the minimum coordinates (ymin1, xmin1) is
calculated. The distance between the centroid and (ymin1, xmin1) represents the radius of the
disc.
(13)
where ROD is the radius of the optic disc.
4.7. Extraction of Optic Cup
The above mentioned FCM clustering with thresholding is applied on the smoothed green
channel for the extraction of OC.
The following algorithm includes four steps [4].
Step1. Randomly initialize the membership matrix (U) that has constraints in Equation (6).
Step2. Calculate centroids (Ci) by using Equation (7).
Step3. Compute dissimilarity between centroids and data points using equation (5). Stop if its
improvement over previous iteration is below a threshold.
Step4. Compute a new U using Equation (6). Go to Step 2[19][20].
The threshold values T2 is calculated using the following equation.
(14)
where data represents the data points of the vessel free green channel and label represents the
cluster value with the highest membership grade.
Since the OC is the brightest portion in the green channel, thresholding with threshold T2 in
im2bw function helps to extract OC. This function returns the binary image forming the object
OC.
The average of the rows and columns forms the centroid (Y2, X2) of the OC.
(15)
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(16)
where m2 is the number of rows and n2 is the number of columns.
From the above coordinates of the optic cup the minimum coordinates (ymin2, xmin2) is
calculated. The Euclidian distance between the centroid and (ymin1, xmin1) returns the radius of
the cup.
ROC = Y2 − ymin2, (17)
where ROC is the radius of the cup.
Figure 6. Extracted Optic cup
The method can be used to extract the OD and OC from right eye and left eye images. Figure 7
and Figure 8 show the results of extractions.
Figure 7 (i) Input image of a right eye (ii) Extracted optic disc (iii) Extracted optic cup
Figure 8 (i) Input image of a left eye (ii) Extracted optic disc (iii) Extracted optic cup
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4.8. Computation of CDR
The manual method uses the ratio of the vertical diameter of OC and OD for the computation of
CDR. From the segmented OD the minimum row coordinate ymin1and maximum row coordinate
ymax1 are calculated. The Euclidian distance between these coordinates is the vertical diameter of
the OD, ODvdiam .
(18)
Similarly from the segmented OC the minimum row coordinate ymin2 and maximum row
coordinate ymax2 are calculated. The Euclidian distance between these coordinates is the vertical
diameter of the OC, OCvdiam.
(19)
The CDR is calculated using the following formula
(20)
The following figure shows the OD vertical diameter ODvdiam and OC vertical diameter
OCvdiam of the input image.
Figure 9 Optic Disc vertical diameter ODvdiam and Optic Cup vertical diameter OCvdiam
4.9 Glaucoma Detection
If the CDR is 0.4 or less refers to a relatively healthy looking optic nerve. If the CDR is greater
than .4 is suspicious of glaucoma. While there is no one CDR that separates normal from
glaucoma, the CDR greater than 0.6 or 0.7 is glaucomatous and often requires further testing to
rule out glaucoma. CDR > .7 is considered as advanced stage glaucoma. As glaucoma progresses,
the CDR enlarges (as more optic nerve fiber dies off), and the patient may start to develop
peripheral vision loss.
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Figure 10 (a) Normal (b) Early Stage Glaucoma (c) Advanced Stage Glaucoma
5. RESULTS AND DISCUSSION
The automatic computation of CDR is required for automatic detection of glaucoma using retinal
fundus images. This study thus brings to light simple but efficient methods for the extraction of
OD and OC in retinal images. The CDR values are also automatically calculated. The new
method is evaluated on the basis of the ground truth data, where, vertical CDR values are
obtained from an expert ophthalmologist. The method was applied on 365 retinal color fundus
images. The performance evaluation is done by making use of the comparison of obtained CDR
with the clinical CDR.
5.1. Image Data Sets
5.1.1. DRION Database
It has 110 retinal images with each image having the resolution of 600 x 400 pixels and the optic
disc annotated by two experts with 36 landmarks. The mean age of the patients was 53.0 years
(standard Deviation 13.05), with 46.2% male and 53.8% female and all of them were Caucasian
ethnicity 23.1% patients had chronic simple glaucoma and 76.9% eye hypertension. The images
were acquired with a colour analogical fundus camera, approximately centered on the ONH and
they were stored in slide format. In order to have the images in digital format, they were digitized
using a HP-PhotoSmart-S20 high-resolution scanner, RGB format, resolution 600x400 and 8
bits/pixel.
5.1.2. The DIARETDB0 Database
The DIARETDB0 Database images were captured using an FOV of 50° and the size of each
image is 1500 x 1152 x 3. Out of the 130 images of the DIARETDB0 database, 20 have normal
architecture and 110 have various types of pathology.
5.1.3. Images from the ophthalmologist
125 images from Giridhar Eye Institute, Kochi was also used in this paper. All the images were
obtained using Carlzeiss fundus camera. In total 5 are normal images and remaining 120 are
diseased and the size of each image is 576 x 768 x 3.
5.2. Implementation
The new algorithm was applied on 365 images obtained from the above mentioned databases and
ophthalmologists.
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5.3. Performance Evaluation
The performance evaluation is done using the following parameters.
5.3.1. Accuracy
The accuracy of the technique was evaluated quantitatively by comparing the obtained vertical
CDR values with ophthalmologists’ ground-truth vertical CDR values. Fifteen examples of
detailed results of performance measurement using FCM clustering combined with thresholding
are displayed in Table I using fifteen test images of DRION database and fifteen test images from
the ophthalmologist.
Table I CDR Comparison Table shows the comparison of clinical CDR values with CDR values
obtained using the new method.
5.3.2. Detection of Glaucoma
Based on the CDR values the images are classified into three categories such as normal, early
stage glaucoma and advanced stage glaucoma. The method is applied on 110 images from
DRION database, 130 images from Diaretdb0 and 125 images from an ophthalmologist. Figure
11 shows the detection of different stages of glaucoma based on CDR.
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Figure11. (a) Input Image (b) Segmented image I- Normal II - Early Stage
Glacoma III- Advanced Stage Glaucoma
6. CONCLUSION
This paper presents a new fuzzy based approach for glaucoma detection. The results presented in
this paper show that the new methodology offers a reliable and robust solution for glaucoma
detection. This automated method is very useful for the automatic screening of retinal images.
However the present method has the following limitations. It is assumed that the OD and OC are
brighter than the surrounding pixels and therefore cannot handle retinal images with a relatively
dark OD. Hence advanced extraction methods are required for future studies and research.
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AUTHORS
Thresiamma Devasia was graduated with Bachelor of Mathematics (BSc.Maths) from
Mahatma University, Kerala, India in 1991, and finished her Master of Computer
Applications (MCA) and M.Phil Computer Science from Alagappa University
Tamilnadu, India in 1995 and 2010, respectively. Currently, she is the Head and
Associate professor, Department of Computer Science at Assumption College
Changanacherry, Kerala, India and working toward her Ph.D. at Cochin University of
Science And Technology on glaucoma detection using image processing. She
completed UGC sponsored minor research project based on image processing. She was
a member of IEEE. Her interest areas include image processing and medical imaging.
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Dr. K.Poulose Jacob, Professor of Computer Science at Cochin University of Science
and Technology since 1994, is currently Pro Vice Chancellor of Cochin University of
Science & Technology. He has presented research papers in several International
Conferences in Europe, USA, UK, Australia and other countries. He has served as a
Member of the Standing Committee of the UGC on Computer Education &
Development. He is the Zonal Coordinator of the DOEACC Society under the
Ministry of Information Technology, Government of India. He serves as a member of
the AICTE expert panel for accreditation and approval. He has been a member of
several academic bodies of different Universities and Institutes. He is on the editorial board of two
international journals in Computer Science. Dr. K.Poulose Jacob is a Professional member of the ACM
(Association for Computing Machinery) and a Life Member of the Computer Society of India.
Dr.Tessamma Thomas received her M.Tech. and Ph.D from Cochin University of
Science and Technology, Cochin-22, India. At present she is working as Professor in
the Department of Electronics, Cochin University of Science and Technology. She has
to her credit more than 100 research papers, in various research fields, published in
International and National journals and conferences. Her areas of interest include
digital signal / image processing, bio medical image processing, super resolution,
content based image retrieval, genomic signal processing, etc.