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.
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
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THR...IJCSES Journal
One common cause of visual impairment among people of working age in the industrialized countries is
Diabetic Retinopathy (DR). Automatic recognition of hard exudates (EXs) which is one of DR lesions in
fundus images can contribute to the diagnosis and screening of DR.The aim of this paper was to
automatically detect those lesions from fundus images. At first,green channel of each original fundus image
was segmented by improved Otsu thresholding based on minimum inner-cluster variance, and candidate
regions of EXs were obtained. Then, we extracted features of candidate regions and selected a subset which
best discriminates EXs from the retinal background by means of logistic regression (LR). The selected
features were subsequently used as inputs to a SVM to get a final segmentation result of EXs in the image.
Our database was composed of 120 images with variable color, brightness, and quality. 70 of them were
used to train the SVM and the remaining 50 to assess the performance of the method. Using a lesion based
criterion, we achieved a mean sensitivity of 95.05% and a mean positive predictive value of 95.37%. With
an image-based criterion, our approach reached a 100% mean sensitivity, 90.9% mean specificity and
96.0% mean accuracy. Furthermore, the average time cost in processing an image is 8.31 seconds. These
results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening
for DR.
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
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
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AUTOMATED DETECTION OF HARD EXUDATES IN FUNDUS IMAGES USING IMPROVED OTSU THR...IJCSES Journal
One common cause of visual impairment among people of working age in the industrialized countries is
Diabetic Retinopathy (DR). Automatic recognition of hard exudates (EXs) which is one of DR lesions in
fundus images can contribute to the diagnosis and screening of DR.The aim of this paper was to
automatically detect those lesions from fundus images. At first,green channel of each original fundus image
was segmented by improved Otsu thresholding based on minimum inner-cluster variance, and candidate
regions of EXs were obtained. Then, we extracted features of candidate regions and selected a subset which
best discriminates EXs from the retinal background by means of logistic regression (LR). The selected
features were subsequently used as inputs to a SVM to get a final segmentation result of EXs in the image.
Our database was composed of 120 images with variable color, brightness, and quality. 70 of them were
used to train the SVM and the remaining 50 to assess the performance of the method. Using a lesion based
criterion, we achieved a mean sensitivity of 95.05% and a mean positive predictive value of 95.37%. With
an image-based criterion, our approach reached a 100% mean sensitivity, 90.9% mean specificity and
96.0% mean accuracy. Furthermore, the average time cost in processing an image is 8.31 seconds. These
results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening
for DR.
International Journal of Computational Engineering Research(IJCER)ijceronline
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.
Automatic identification and classification of microaneurysms for detection o...eSAT Journals
Abstract Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. He experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, we have fabricated a prototype of automatic headlight dimmer. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. It also eliminates the requirement of manual switching by the driver which is not done at all times. The construction, working and the advantages of this prototype model is discussed in detail in this paper. Keywords: Headlight, automatic, dimmer, control, high beam, low beam, Kelvin (K).
Automatic Detection of Diabetic Maculopathy from Funduas Images Using Image A...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
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.
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
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.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
Haemorrhage Detection and Classification: A ReviewIJERA Editor
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
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
Diabetic Retinopathy Detection using Neural Networkingijtsrd
The clinical and laboratory studies states that diabetic retinopathy is the major cause of permanent blindness among the aged personalities. The problem with this disease is that there is no cure for it and the only thing we can do is to detect the disease as soon as possible in order to prevent further loss of vision. In this system we propose a CNN approach for diagnosing DR from retinal images and classifying the stages of the disease .The classification is done based on the haemorrhages, micro aneurysms present in the retinal image. We train this network using a high end graphics processor unit GPU using kaggle data set and the disease classification is done, hence we can identify the the disease and prevent the further loss of vision. N Rahul | Roy Eluvathingal | Sanith Jayan K | Mr. Anil Antony "Diabetic Retinopathy Detection using Neural Networking" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31487.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31487/diabetic-retinopathy-detection-using-neural-networking/n-rahul
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...iosrjce
To diagnosis of Diabetic Retinopathy (DR) it is the prime cause of blindness in the working age
population of the world. Detection method is proposed to detect dark or red lesions such as microaneurysms
and hemorrhages in fundus images.Developed during this work, this first is for collection of lesion data
information and was used by the ophthalmologist in marking images for database while the automatic
diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter
three of this report. The primary aim of this project is to develop a system that will be able to identify patients
with BDR and PDR from either colour image or grey level image obtained from the retina of the patient. The
algorithm was tested fundus images. The Operating Characteristics (ROC) was determined for red spot lesion
and bleeding, while cross over points were only detected leaving further classification as part of future work
needed to complete this global project. Sensitivity and specificity was calculated for the algorithm is given
respectively as 96.3% and 95.1%
In recent days, skin cancer is seen as one of the most Hazardous form of the cancer found in Humans. Skin Cancer is a malignant tumor that grows in the skin cells. It can be affected mostly by the reason of skin burn caused by sunlight. Early detection and treatment of Skin cancer can significantly improve patient outcome. Automatic detection is one of the most challenging research areas that can be used for early detection of such vital cancer. A person’s in which they have inadequate amount of melanoma will be exposed to the risk of sun burns and the ultra violet rays from the sun will be affected that body. Malignant melanomas is a type of melanoma that has irregular borders, color variations so analyze the shape, color and texture of the skin lesion is important for the early detection. It can have the components of an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction and finally classification. Finally the result show that the system is efficient achieving classification of the lesion as either melanoma or Non melanoma causes.
Detection and Grading of Diabetic Maculopathy Automatically in Digital Retina...paperpublications3
Abstract: Diabetic Retinopathy (DR) is a critical eye disease which can be regarded as manifestation of diabetes on the retina the symptoms can blur or distort the patient’s vision and are a main cause of blindness. Exudates are one of the signs of Diabetic Retinopathy. If the disease is detected early and treated promptly many of the visual loss can be prevented. This paper explains the development of an automatic fundus image processing and analytic system to facilitate diagnosis of the ophthalmologists. The algorithms to detect the optic disc; blood vessels and exudates are investigated. The proposed system extracts macula from digital retinal image using the optic disc location. Many common features such as intensity, geometric and correlations are used to distinguish between them. The system uses GLCM for feature extraction. The system uses a SVM based classifier to differentiate the retinal images in different stages of maculopathy by using the macula co-ordinates and exudates feature set.
Automatic identification and classification of microaneurysms for detection o...eSAT Journals
Abstract Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. He experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, we have fabricated a prototype of automatic headlight dimmer. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. It also eliminates the requirement of manual switching by the driver which is not done at all times. The construction, working and the advantages of this prototype model is discussed in detail in this paper. Keywords: Headlight, automatic, dimmer, control, high beam, low beam, Kelvin (K).
Automatic Detection of Diabetic Maculopathy from Funduas Images Using Image A...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
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.
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
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.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
Haemorrhage Detection and Classification: A ReviewIJERA Editor
In Indian population, the count of diabetic peoples gets increasing day by day. Due to improper balance of insulin in the human body causes Diabetic. The most common symptom of the person with diabetes is diabetic retinopathy, which leads to blindness. The effect due to DR can reduce by early detection of Haemorrhages and treated at an early stage. In recent year, there is an increased interest in the field of medical image processing. Many researchers have developed advanced algorithms for Haemorrhage detection using fundus images. In proposed paper, we discuss various methods for Haemorrhage detection and classification.
Retinal Macular Edema Detection Using Optical Coherence Tomography ImagesIOSRJVSP
Macular Edema affects around 20 million people of the world each year. Optical Coherence Tomography (OCT), a non-invasive eye-imaging modality, is capable of detecting Macular Edema both in its early and advanced stages. In this paper, an algorithm which detects Macular Edema from OCT images has been presented. Initially the images are filtered to de-noise them. Then, the retinal layers - Inner Limiting Membrane (ILM) and Retinal Pigment Epithelium (RPE) are segmented using Graph Theory method. Region splitting is performed on the OCT scan and the thickness between the two layers in the different regions are determined. Area enclosed between the two layers is also estimated. Support Vector Machine, a binary classifier is used to draw a classification between normal and abnormal OCT scans. Region-wise thickness, a few Haralick’s features, area between ILM and RPE and a few wavelet features are used to train the classifier. The classifier yielded an accuracy of 95% and a sensitivity of 100%. Thus, this algorithm can be used by ophthalmologists in early detection of Macular Edema.
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
Diabetic Retinopathy Detection using Neural Networkingijtsrd
The clinical and laboratory studies states that diabetic retinopathy is the major cause of permanent blindness among the aged personalities. The problem with this disease is that there is no cure for it and the only thing we can do is to detect the disease as soon as possible in order to prevent further loss of vision. In this system we propose a CNN approach for diagnosing DR from retinal images and classifying the stages of the disease .The classification is done based on the haemorrhages, micro aneurysms present in the retinal image. We train this network using a high end graphics processor unit GPU using kaggle data set and the disease classification is done, hence we can identify the the disease and prevent the further loss of vision. N Rahul | Roy Eluvathingal | Sanith Jayan K | Mr. Anil Antony "Diabetic Retinopathy Detection using Neural Networking" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31487.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31487/diabetic-retinopathy-detection-using-neural-networking/n-rahul
Automatic Detection of Non-Proliferative Diabetic Retinopathy Using Fundus Im...iosrjce
To diagnosis of Diabetic Retinopathy (DR) it is the prime cause of blindness in the working age
population of the world. Detection method is proposed to detect dark or red lesions such as microaneurysms
and hemorrhages in fundus images.Developed during this work, this first is for collection of lesion data
information and was used by the ophthalmologist in marking images for database while the automatic
diagnosing and displaying the diagnosis result in a more friendly user interface and is as shown in chapter
three of this report. The primary aim of this project is to develop a system that will be able to identify patients
with BDR and PDR from either colour image or grey level image obtained from the retina of the patient. The
algorithm was tested fundus images. The Operating Characteristics (ROC) was determined for red spot lesion
and bleeding, while cross over points were only detected leaving further classification as part of future work
needed to complete this global project. Sensitivity and specificity was calculated for the algorithm is given
respectively as 96.3% and 95.1%
In recent days, skin cancer is seen as one of the most Hazardous form of the cancer found in Humans. Skin Cancer is a malignant tumor that grows in the skin cells. It can be affected mostly by the reason of skin burn caused by sunlight. Early detection and treatment of Skin cancer can significantly improve patient outcome. Automatic detection is one of the most challenging research areas that can be used for early detection of such vital cancer. A person’s in which they have inadequate amount of melanoma will be exposed to the risk of sun burns and the ultra violet rays from the sun will be affected that body. Malignant melanomas is a type of melanoma that has irregular borders, color variations so analyze the shape, color and texture of the skin lesion is important for the early detection. It can have the components of an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction and finally classification. Finally the result show that the system is efficient achieving classification of the lesion as either melanoma or Non melanoma causes.
Detection and Grading of Diabetic Maculopathy Automatically in Digital Retina...paperpublications3
Abstract: Diabetic Retinopathy (DR) is a critical eye disease which can be regarded as manifestation of diabetes on the retina the symptoms can blur or distort the patient’s vision and are a main cause of blindness. Exudates are one of the signs of Diabetic Retinopathy. If the disease is detected early and treated promptly many of the visual loss can be prevented. This paper explains the development of an automatic fundus image processing and analytic system to facilitate diagnosis of the ophthalmologists. The algorithms to detect the optic disc; blood vessels and exudates are investigated. The proposed system extracts macula from digital retinal image using the optic disc location. Many common features such as intensity, geometric and correlations are used to distinguish between them. The system uses GLCM for feature extraction. The system uses a SVM based classifier to differentiate the retinal images in different stages of maculopathy by using the macula co-ordinates and exudates feature set.
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%
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.
The legal cause of blindness for the workingage
population in western countries is Diabetic Retinopathy - a
complication of diabetes mellitus - is a severe and wide- spread
eye disease. Digital color fundus images are becoming
increasingly important for the diagnosis of Diabetic Retinopathy.
In order to facilitate and improve diagnosis in different ways, this
fact opens the possibility of applying image processing techniques
.Microaneurysms is the earliest sign of DR, therefore an
algorithm able to automatically detect the microaneurysms in
fundus image captured. Since microaneurysms is a necessary
preprocessing step for a correct diagnosis. Some methods that
address this problem can be found in the literature but they have
some drawbacks like accuracy or speed. The aim of this thesis is
to develop and test a new method for detecting the
microaneurysms in retina images. To do so preprocessing, gray
level 2D feature based vessel extraction is done using neural
network by using extra neurons which is evaluated on DRIVE
database which is superior than rulebased methods. To identify
microaneurysms in an image morphological opening and image
enhancement is performed. The complete algorithm is developed
by using a MATLAB implementation and the diagnosis in an
image can be estimated with the better accuracy and in shorter
time than previous techniques
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.
An Automated Systems for the Detection of Macular Ischemia based-Diabetic Ret...iosrjce
The proposed methodology in this paper marks out application for automatic detection of eye
diseases called Macular Ischemia using image processing techniques. In semi urban and rural areas large
percentages of people suffer from various eye diseases. For diagnoses of various eye diseases, Image processing
technique is used. . Diseases occur in Macula from retinal images have a huge type of textures, shapes and at
times they are difficult to be recognised and identified by doctors. Thus we are trying to optimize and develop
such system which is based on smart image recognition/classification algorithms. This proposed system
provides accuracy, uniformity and speed in performance and a high credence coefficient in results interpreting.
Keywords: Macular Ischemia, diagnosis, textures, consistence
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
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 .
An improved dynamic-layered classification of retinal diseasesIAESIJAI
Retina is main part of the human eye and every disease shows the effect on retina. Eye diseases such as choroidal neovascularization (CNV), DRUSEN, diabetic macular edema (DME) are the main retinal diseases that damage the retina and if these damages are identified in the later stages, it is very difficult to reverse the vision for these retinal diseases. Optical coherence tomography (OCT) is a non-nosy image testing for finding the retinal diseases. OCT mainly collects the cross-section images of retina. Deep learning (DL) is used to analyze the patterns in several complex research applications especially in the disease prediction. In DL, multiple layers give the accurate detection of abnormalities in the retinal images. In this paper, an improved dynamic-layered classification (IDLC) is introduced to classify retinal diseases based on their abnormality. Image filters are used to filter the noise present in the input images. ResNet is the pre-trained model which is used to train the features of retinal diseases. Convolutional neural networks (CNN) are the DL model used to analyze the OCT image. The dataset consists of three types of OCT disease datasets from Kaggle. Evaluation results show the performance of IDLC compared with state-of-art algorithms. A better performance is obtained by using the IDLC and achieved the better accuracy.
Diabetic retinopathy is a severe vision complication of
diabetes and it is one of the main causes of blindness all over the
world. Early detection of diabetic retinopathy is essential to cope
with this adverse effect. Microaneurysm is one of the early signs
of diabetic retinopathy. So the presence of microaneurysm
detection is a prerequisite for early diagnosis of diabetic
retinopathy. In this paper, we have proposed a simple
morphological method for the detection of microaneurysm that
uses top-hat transform. Our method can detect the faint
microaneurysm at low resolution due to contrast enhancement
and noise reduction as preprocessing. We also compare the
results of different retinopathy detection techniques.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
A review on implementation of algorithms for detection of diabetic retinopathyeSAT 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
Similar to An Efficient Integrated Approach for the Detection of Exudates and Diabetic Maculopathy in Colour fundus Images (20)
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Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Submission Deadline : April 08, 2023
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline
Advanced Computing: An International Journal (ACIJ
)
is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advancedcomputing. The journal focuses on all technical and practical aspects of high performancecomputing, green computing, pervasive computing, cloud computing etc. The goal of this journalis to bring together researchers anda practitioners from academia and industry to focus onunderstanding advances in computing and establishing new collaborations in these areas
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline.com
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An Efficient Integrated Approach for the Detection of Exudates and Diabetic Maculopathy in Colour fundus Images
1. Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.5, September 2012
DOI : 10.5121/acij.2012.3509 83
An Efficient Integrated Approach for the
Detection of Exudates and Diabetic Maculopathy
in Colour fundus Images
B.Ramasubramanian1
and G.Mahendran2
1
Department of Electronics and Communication Engineering,
Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India.
ramatech87@gmail.com
2
Department of Electronics and Communication Engineering,
Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India.
ABSTRACT
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 non-
exudates 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.
Keywords
CIE Lab Colour Space, CLAHE, Diabetic Retinopathy (DR), Exudates, GLCM, K-Means Clustering,
SVM.
1. INTRODUCTION
Diabetic Retinopathy is the common retinal complication associated with diabetes. It is a major
cause of blindness in both middle and advanced age groups [1]. The International Diabetes
Federation reports that over 50 million people in India have this disease and it is growing
rapidly (IDF 2009a) [2]. The estimated prevalence of diabetes for all age groups worldwide was
2.8% in 2000 and 4.4% in 2030 meaning that the total number of diabetes patients is forecasted
to rise from 171 million in 2000 to 366 million in 2030 [3]. Therefore regular screening is the
most efficient way of reducing the vision loss.
Diabetic Retinopathy is mainly caused by the changes in the blood vessels of the retina
due to increased blood glucose level. Exudates are one of the primary sign of Diabetic
Retinopathy [5]. Exudates are yellow-white lesions with relatively distinct margins. Exudates
are lipids and proteins that deposits and leaks from the damaged blood vessels within the retina.
Detection of Exudates by ophthalmologists is a laborious process as they have to spend a great
deal of time in manual analysis and diagnosis. Moreover, manual detection requires using
chemical dilation material which takes time and has negative side effects on patients. Hence
automatic screening techniques for exudates detection have great significance in saving costs,
time and labour in addition to avoiding the side effects on patients.
2. Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.5, September 2012
84
Digital Colour fundus images are widely used by ophthalmologists for diagnosing
Diabetic Retinopathy. DR also causes numerous abnormalities like microaneurysm,
haemorrhages, cotton wool spots, neo-vascularisation and in later stages, retinal detachment.
Figure 1. Diabetic Retinopathy image with various typical components.
Figure 1 depicts a typical retinal image labelled with various feature components of Diabetic
Retinopathy. Microaneurysm are small saccular pouches and appears as small red dots. This
may lead to big blood clots called haemorrhages. The bright circular region from where the
blood vessels emanate is called optic disk (OD). Macula is the centre portion of the retina and
has photoreceptors called cons that are highly sensitive to colour and responsible for perceiving
fine details. It is situated at the posterior pole temporal to the optic disk. The fovea defines the
centre of the macula and is the region of highest visual acuity.
2. PROPOSED METHOD FOR THE DETECTION OF EXUDATES IN COLOUR
FUNDUS IMAGES:
2.1 State of Art:
Alireza Osareh et al [4] proposed a method for automatic identification of exudates based on
computational Intelligence technique The colour retinal images were segmented using fuzzy c-
means clustering. Feature vector were extracted and classified using multilayer neural network
classifier.
Akara Sopharak et al [5] reported the result of an automated detection of exudates from
low contrast digital images of retinopathy patients with non-dilated pupils by Fuzzy C-Means
clustering. Four features such as intensity, standard deviation on intensity, hue and a number of
edge pixels were extracted and applied as input to coarse segmentation using FCM clustering
method. The detected result were validated with expert ophthalmologists hand drawn ground
truths. Sensitivity, Specificity, positive predictive value (PPV) , positive likelihood ratio (PLR)
and accuracy were used to evaluate the overall performance of the system.
Niemeijer et al [6] distinguished the bright lesion like exudates, cotton wool spots and
drusen from colour retinal images. In the first step, pixels were classified, resulting in a
probability map that included the probability of each pixel to be part of a bright lesion. Then,
pixels with high probability were grouped into probable lesion pixel clusters. Based on cluster
characteristics, each cluster was assigned a probability indicating the likelihood that the cluster
was a true bright lesion. Finally these clusters were classified as exudates, cotton wool spots or
drusen. Sensitivities and specificities of the annotations on the 300 images by the automated
system were obtained.
Akara sopharak et al [7] proposed a series of experiments on feature selection and
exudates classification using naive bayes and Support Vector Machine (SVM) Classifiers. At
first, they used naive bayes model to a training set consisting of 15 features extracted from
3. Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.5, September 2012
85
positive and negative examples of exudates pixels. Next, to obtain the best SVM, they used the
best feature set from the naive bayes classifier and continually appended the removed features
to the classifier. For each combination of features, they carried out a grid search to find the best
combination of hyper parameters like tolerance for training error and radial basis function
width. They compared the best naive bayes and SVM classifier to a Nearest Neighbour
classifier. They proved that the naive bayes and SVM classifiers executed better than the NN
classifier.
Walter et al [8] identified exudates from green channel of the retinal images according
to their gray level variation. The exudates contour were determined using mathematical
morphology techniques. This method used three parameters: size of the local window and two
threshold value. Exudates regions were initially found using first threshold value. The second
threshold represents the minimum value, by which a candidate pixel must differ from its
surrounding background to be classified as exudates. The author achieved a sensitivity of
92.8% and predictivity of 92.4% against a set of 15 abnormal retinal images. However the
author ignored some types of errors on the border of the segmented exudates in their reported
performances and did not discriminate exudates from cotton wool spots.
Sinthanayothin et al [9] reported the result of an automated detection of Diabetic
Retinopathy by Recursive Region Growing techniques on a 10X10 window using selected
threshold values. In the pre-processing steps, adaptive, local, contrast enhancement is applied.
The author reported a sensitivity of 88.5% and specificity of 99.7% for the detection of
exudates against a small dataset comprising 21 abnormal and 9 normal retinal images.
Phillips et al [10] identified the exudates by using Global and local thresholding. The
input images were pre-processed to eliminate photographic non-uniformities and the contrast of
the exudates was then enhanced. The lesion based sensitivity of this technique was reported
between 61% and 100% based on 14 images. A drawback of this method was that other bright
lesions (such as cotton wool spots) could be identified mistakenly.
2.2.Image Acquisition:
To evaluate the performance of this method, the digital retinal images were acquired
using Topcon TRC-50 EX camera with a 50˚ field of view at Aravind Eye hospital, Madurai.
2.3. Pre-processing:
Colour fundus images often show important lighting variation, poor contrast and noise.
In order to reduce these imperfection [11] and generate images more suitable for extracting the
pixel features in the classification process, a pre-processing comprising the following step is
applied. 1) RGB to HSI conversion 2) Median Filtering 3) Contrast Limited Adaptive
Histogram Equalization (CLAHE).
2.3.1 RGB to HSI Conversion:
The input retinal images in RGB Colour space are converted to HSI colour space. The noise in
the images are due to the uneven distribution of the intensity(I) component.
2.3.2 Median Filtering:
In order to uniformly distribute the intensity throughout the image, the I-component of HSI
colour space is extracted and filtered out through a 3X3 median filter.
2.3.3. Contrast Limited Adaptive Histogram Equalization (CLAHE):
The contrast limited adaptive histogram equalization is applied on the filtered I-component of
the image [12]. The histogram equalized I component is combined with HS component and
transformed back to the original RGB colour space.
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2.4. Image Segmentation based on K-Means:
In this approach, we present a novel image segmentation based on colour features from the
images. The work is divided into two stages: First, enhancing the colour separation is done by
extracting the a*b* components from the L*a*b* colour space of the pre-processed image.
Then, the regions are grouped into a set of five clusters using K-means Clustering algorithm.
By this two step process, we reduce the computational cost avoiding feature calculation for
every pixel in the image[13].
The entire process can be summarized in following steps:
Step 1: Read the image. Figure 2 shows the example input retinal image with exudates.
Step 2: Convert the image from RGB colour space to L*a*b* colour space (Figure 3). L*a*b*
colour space helps us to classify the colour differences. It is derived from the CIE XYZ
tristimulus values. L*a*b* colour space consists of a Luminosity layer L*, chromaticity layer
a* indicating where the colour falls along the red-green axis, chromaticity layer b* indicating
where the colour falls along the blue-yellow axis. All of the colour information is in the a* and
b* layer. The difference between two colours can be measured using the Euclidean distance.
Step 3: Segment the colours in a*b* space using K-means clustering. Clustering is a way to
separate groups of objects. K-Means Clustering treats each object as having a location in space.
It finds partition such that objects within each cluster are close to each other as possible and as
far from other objects in other clusters as possible. The algorithm requires that we specify the
number of clusters to be partitioned and a distance metric to quantify how close two objects are
to each others. Since the colour information exist in the a*b* space, our objects are pixels with
a* and b* values. Use K-means to cluster the objects into five clusters using the Euclidean
distance metric.
Figure 2. Original Image Figure 3. CIE Lab Colour space
conversion.
Step 4: Label every pixel in the image using the result from K-means For every objects in the
input, K-means returns an index corresponding to a cluster. Label every pixel in the image with
its cluster index.
Step 5: Create images that segment the images by colour.
Step 6: Since the Optic Disc and Exudates are homogenous in their colour property, cluster
possessing Optic Disc is localized for further processing.
2.5. Feature Extraction:
To classify the localized segmented image into exudates and Non-exudates, a number
of features based on colour and texture are extracted using Gray Level Co-occurrence Matrix
(GLCM) . GLCM is a tabulation of how often different combination of pixel brightness values
occur in a pixel pair in an image. Each element (i, j) in GLCM specifies the number of times
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that the pixel with value i occurred horizontally adjacent to a pixel with value j. The resulting
matrix was analysed and based on the existing information, the feature vectors are formed [14].
Based on texture , the following features are extracted:
Table 1. Feature Extraction
S.No. FEATURES FORMULA
01 CONTRAST
02 CORRELATION
03 CLUSTER PROMINENCE
04 CLUSTER SHADE
05 DISSIMILARITY
06 ENTROPY
07 ENERGY
08 HOMOGENEITY
09 SUM OF SQUARES
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2.6. Classification using SVM:
To classify these segmented region into Exudates and Non-Exudates, we make use of well-
known efficient Support Vector Machine (SVM) Classifier. The System is trained using the
above calculated feature vectors and the method is tested on 70 abnormal images and 30 normal
images. SVM classifier outperforms well when compared with other types of classifiers.
2.7. Detection of Diabetic Maculopathy :
For the Detection of Diabetic Maculopathy, the Green Component is extracted from the colour
fundus images. It is then pre-processed using the median filter and Adaptive Histogram
Equalization. The Green component is applied to Bottom-hat transform and the result is
subtracted with the top-hat transformed image. By applying these transform, macula, which is
the darkest region of an image is detected. If the exudates is present in the macula, then it
indicates the presence of Diabetic Maculopathy.
2.8. Result:
2.8.1 Exudates Detection using SVM:
In this approach, we have investigated and proposed a method to automatically extract exudates
from Diabetic Retinopathy images. The pre-processed colour retinal image is segmented into
five cluster using colour K-Means Clustering algorithm. The cluster containing Optic Disc is
selected and features are extracted. The segmented colour image with Optic Disc is shown in
Figure 4.b.
Figure 4.a. Original Image b. Selected Cluster with Optic Disc.
In the selected cluster, feature vector are extracted using Gray Level Co-occurrence Matrix.
With the help of these features, the selected cluster is classified into Normal (Non- Exudates) or
Abnormal (Exudates) using Support Vector Machine (SVM) Classifier. The success rate is
found to be 96%.
Figure 5 shows the entire GUI based system for the detection of Exudates in Diabetic
Retinopathy Images.
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89
Figure 5. Detection of Exudates in Diabetic Retinopathy Images.
2.8.2. Detection of Diabetic Maculopathy:
Macula is the darkest part in the retinal image. This region is localized using morphological
operation. If exudates is present in this macula region, then it indicates the presence of Diabetic
Maculopathy. If exudates is not present in this region, then it shows the absence of Diabetic
Maculopathy.
Figure 6.a shows the colour fundus image affected with Diabetic Maculopathy. The centre
darkest region is the macula. In our approach, the Diabetic Maculopathy is detected using
morphological operation and it is indicated in Figure 6.b using the rectangular box.
Figure 6. a) Colour fundus Image b) Detection of Diabetic Maculopathy
3. CONCLUSIONS:
The diabetic retinopathy images were collected from STARE and DRIVE database. Exudates
are one of the earlier signs of diabetic retinopathy. The low contrast digital image is enhanced
using Contrast Limited Adaptive Histogram Equalization (CLAHE). The noise are removed
from the images using median filter. The Contrast enhanced colour image is segmented using
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K-means clustering, which is one of the simplest unsupervised learning algorithm for image
segmentation. algorithm. K-means clustering takes less computational time compared to FCM.
It provides more colour information from which the result of classification will be improved.
To Classify these segmented image into exudates and non-Exudates, a set of features based on
texture and colour are extracted using Gray Level Co-Occurrence Matrix (GLCM). The
selected features are classified into exudates and non-exudates using Support Vector Machine
(SVM) Classifiers. Also the detection of Diabetic Maculopathy, which is the severe stage of
Diabetic Retinopathy is performed using morphological operation. The method is evaluated on
70 abnormal and 30 normal images. Out of these 100 images, 96 images were detected
successfully and thus a success rate of 96% was obtained and Diabetic Maculopathy are
detected with 100% success rate.
ACKNOWLEDGEMENTS
Our Sincere thanks to Department of Ophthalmology, Vasan Eye Care Hospital, Chennai,
TamilNadu for providing the images and necessary details.
REFERENCES
[1] Olson. J. A, Strachana. F.M, Hipwell. J. H, “A comparative evaluation of digital imaging,
retinal photography and optometrist examination in screening for diabetic retinopathy”
Journal on Diabet Med. Vol. 20, No. 7 .pp. 528- 534, July 2003.
[2] International Diabetic Federation (IDF), 2009a, Latest diabetes figures paint grim global
picture.
[3] Saiprasad Ravishankar, Arpit Jain, Anurag Mittal, “Automated feature extraction for early
detection of Diabetic Retinopathy in fundus images”. IEEE Conference on Computer vision and
pattern Recognition, pp. 210-217, August 2009.
[4] Alireza Osareh, Bita shadgar and Richard Markham,“A computational intelligence based
approach for detection of exudates in Diabetic Retinopathy Images”, IEEE Trans. on
Information Technology in Biomedicines, vol. 13, no. 4, pp. 535-545, July 2009.
[5] Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, “Automatic Exudate Detection from
Non-dilated Diabetic Retinopathy retinal images using Fuzzy C-Means Clustering” Journal of
Sensors, vol.9, No. 3, pp 2148- 2161, March 2009.
[6] Niemeijer. B.V, Ginnekan. S. R, Russell. M, and M. D. Abramoff, “Automated detection and
differentiation of drusen, exudates and cotton- wool spots in digital color fundus photographs
for diabetic retinopathy diagnosis”, Journal on Investigate Ophthalmol. And Visual Science.,
vol. 48, No. 2 pp. 2260-2267, 2007.
[7] Akara Sopharak, Mathew N. Dailey, Bunyarit Uyyanonvara, Sarah Barman, Tom
Williamson,Yin Aye Moe, “Machine Learning approach to automatic Exudates detection in
retinal images from diabetic patients”, Journal of Modern optics,Vol. 57, No. 2, pp. 124-135,
Nov 2011.
[8] T. Walter, J.Klein, P.Massin and A.Erginary, “A Contribution of image processing to the
diagnosis of Diabetic Retinopathy detection of exudates in color fundus images of the human
retina”, IEEE Trans. On Med. images, vol. 21, no. 10, pp. 1236-1243, 2002.
[9] C. Sinthanayothin, “Image analysis for automatic diagnosis of Diabetic Retinopathy”, Journal
of Medical Science, Vol. 35,No. 5, pp. 1491-1501, Jan 2011.
[10] Fleming. AD, Philips. S, Goatman. KA, Williams. GJ, Olson. JA, sharp. PF, “Automated
detection of exudates for Diabetic Retinopathy Screening”, Journal of Phys. Med. Bio., vol. 52,
no. 24, pp. 7385-7396, 2007.
[11] Guoliang Fang, Nan Yang, Huchuan Lu and Kaisong Li, “Automatic Segmentation of Hard
Exudates in fundus images based on Boosted Soft Segmentation”, International Conference on
Intelligent Control and Information Processing, pp. 633-638, Sept 2010.
9. Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.5, September 2012
91
[12] Pizer. S.M. “The Medical Image Display and analysis group at the university of North
Carolina:Reminiscences and philosophy ” IEEE Trans On Medical Imaging, vol. 22, no. 1, pp.
2-10, April 2003.
[13] Plissiti.M.E., Nikar.C, Charchanti.A, “Automated detection of cell nuclei in pap smear images
using morphological reconstruction and clustering” IEEE Trans. On Information Technology in
Biomedicine, vol.15,no. 2, pp. 233-241, March 2011.
.[14] Seongijin park, Bohyoung Kim, Jeongjin Loe“ GGO nodule volume preserving Non-rigid Lung
Registration using GLCM texture analysis”, IEEE Trans. On Biomedical Engg., vol. 58, no. 10,
pp. 2885-2894, sept 2011.
[15] Kandaswamy.U, Adjerch.D.A, Lee.M.C, “Efficient Texture analysis of SAR imagery”, IEEE
Trans. On Geoscience and Remote Sensing, vol. 43, no. 9,pp. 2075-2083, August 2005..
[16] Tobin.K.N, Chaum.E, Govindasamy.V.P, “Detection of anatomic structures in human retinal
imagery”
IEEE Transactions on medical imaging, vol. 26, no. 12,pp. 1729-1739, December 2007.
[17] Gwenole Quellec, Stephen R. Russell, and Michael D. Abramoff, Senior Member, IEEE
“Optimal Filter Framework for Automated, Instantaneous Detection of Lesions in Retinal
Images” IEEE Trans. on medical imaging, vol. 30, no. 2,pp. 523-533, February 2011.
[18] Akara Sopharak, Bunyarit Uyyanonvara, sarah Barman, “Comparative analysis of automatic
exudates detection algorithms”, Proceedings of the world congress on Engg., Vol I, Dec 2011.
[19] Doaa Youssef, Nahed Solouma, Amr El-dib, Mai Mabrouk, “New Feature-Based Detection of
Blood Vessels and Exudates in Color Fundus Images“ IEEE conference on Image Processing
Theory, Tools and Applications,2010,vol.16,pp.294-299.
Author
B.Ramasubramanian received his B.E Degree from Syed Ammal Engineering College,
Ramanathapuram, TamilNadu, India in 2008 and M. Tech Degree from B. S. Abdur
Rahman University, Chennai, TamilNadu, India in 2012. He is currently an Assistant
Professor in Syed Ammal Engineering College, Ramanathapuram. His Research area
includes medical image processing, Digital Signal Processing and Machine Learning.
G. Mahendran received his B.E Degree from Mohamed Sathak Engineering College, Ramanathapuram,
TamilNadu, India and M. E Degree from Mohamed Sathak Engineering College, TamilNadu, India. He
is currently an Assistant Professor in Syed Ammal Engineering College, Ramanathapuram. His
Research area includes Digital Image processing and Digital Signal Processing..