Diabetic Retinopathy Detection System from Retinal Imagesijtsrd
Diabetes Mellitus is a disorder in metabolism of carbohydrates, and due to lack of the pancreatic hormone insulin sugars in the body are not oxidized to produce energy. Diabetic Retinopathy is a disorder of the retina resulting in impairment or vision loss. Improper blood sugar control is the main cause of diabetic retinopathy. That is the reason why early detection of retinopathy is crucial to prevent vision loss. Appearance of exudates, microaneurysms and hemorrhages are the early indications. In this study, we propose an algorithm for detection and classification of diabetic retinopathy. The proposed algorithm is based on the combination of various image processing techniques, which includes Contrast Limited Adaptive Histogram Equalization, Green channelization, Filtering and Thresholding. The objective measurements such as homogeneity, entropy, contrast, energy, dissimilarity, asm, correlation, mean and standard deviation are computed from processed images. These measurements are finally fed to Support Vector Machine and k Nearest Neighbors classifiers for classification and their results were analysed and compared. Aditi Devanand Lotliker | Amit Patil "Diabetic Retinopathy Detection System from Retinal Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38353.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38353/diabetic-retinopathy-detection-system-from-retinal-images/aditi-devanand-lotliker
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
An Amalgamation-Based System for Micro aneurysm Detection and Diabetic Retino...IJMER
We propose an ensemble-based framework to improve microaneurysm detection. Unlike
the well-known approach of considering the output of multiple classifiers, we propose a combination of
internal components of microaneurysm detectors, namely preprocessing methods and candidate
extractors. We have evaluated our approach for microaneurysm detection in an online competition,
where this algorithm is currently ranked as first, and also on two other databases.
Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopath...ITIIIndustries
With the improvement in IT industry, more and more application of computer software is introduced in teaching and learning. In this paper, we discuss the development process of such software. Diabetic Retinopathy is a common complication for diabetic patients. It may cause sight loss if not treated early. There are several stages of this disease. Fundus imagery is required to identify the stage and severity of the disease. Due to the lack of proper dataset of the fundus images and proper annotation, it is very difficult to perform research on this topic. Moreover, medical students are often facing difficulty with identifying the diseases in later stage of their practice as they may not have seen a sample of all of the stages of Diabetic Retinopathy problems. To mitigate the problem, we have collected fundus images from different geographic area of Bangladesh and designed an annotation software to store information about the patient, the infection level and their locations in the images. Sometimes, it is difficult to select all appropriate pixels of the infected region. To resolve the issue, we have introduced a K nearest neighbor (KNN) based technique to accurately select the region of interest (ROI). Once an expert (ophthalmologist) has annotated the images, the software can be used by the students for learning.
A Novel Advanced Approach Using Morphological Image Processing Technique for ...CSCJournals
Diabetic retinopathy (DR) is a common complication of diabetes mellitus and can lead to irreversible blindness. To date, DR is the leading cause of blindness and visual impairment among working adults globally. However, this blindness can be prevented if DR is detected early. Diabetes mellitus slowly affects the retina by damaging retinal blood vessels and leading to microaneurysms. The retinal images give detailed information about the health status of the visual system. Analysis of retinal image is important for an understanding of the stages of Diabetic retinopathy. Microaneurysms observed that appear in retina images, usually, the initial visible sign of DR, if detected early and properly treated can prevent DR complications, including blindness. In this research work, an advanced image modal enhancement comprises of a Contrast Limited Adaptive Histogram Equalization (CLAHE), through morphological image, processing technique with final extraction algorithm is proposed. CLAHE is responsible for the detection, and removal of the retinal optical disk. While the microaneurysm initial indicators are detected by using morphological image processing techniques. The extensive evaluation of the proposed advanced model conducted for microaneurysm detection depicts all stages of DR with an increase in the number of data set related to noise in the image. The microaneurysms noise is associated with stage of retina diseases as well as its early possible diagnosis. Evaluation is also conducted against the proposed model to measure its performance in terms of accuracy, sensitivity as well as specificity in real-time. The results show the test image attained 99.7% accuracy for a real-time database that is better compared with anty colony-based method. A sensitivity of 81% with a specificity of 90% was achieved for the detection of microaneurysms for the e-optha database. The detection of several microaneurysms correlates with stages of DR that prove an analysis of detecting its different stages. As well as it reaches our goal of early detection of DR with high performance in accuracy.
Diabetic Retinopathy Detection System from Retinal Imagesijtsrd
Diabetes Mellitus is a disorder in metabolism of carbohydrates, and due to lack of the pancreatic hormone insulin sugars in the body are not oxidized to produce energy. Diabetic Retinopathy is a disorder of the retina resulting in impairment or vision loss. Improper blood sugar control is the main cause of diabetic retinopathy. That is the reason why early detection of retinopathy is crucial to prevent vision loss. Appearance of exudates, microaneurysms and hemorrhages are the early indications. In this study, we propose an algorithm for detection and classification of diabetic retinopathy. The proposed algorithm is based on the combination of various image processing techniques, which includes Contrast Limited Adaptive Histogram Equalization, Green channelization, Filtering and Thresholding. The objective measurements such as homogeneity, entropy, contrast, energy, dissimilarity, asm, correlation, mean and standard deviation are computed from processed images. These measurements are finally fed to Support Vector Machine and k Nearest Neighbors classifiers for classification and their results were analysed and compared. Aditi Devanand Lotliker | Amit Patil "Diabetic Retinopathy Detection System from Retinal Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38353.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38353/diabetic-retinopathy-detection-system-from-retinal-images/aditi-devanand-lotliker
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
An Amalgamation-Based System for Micro aneurysm Detection and Diabetic Retino...IJMER
We propose an ensemble-based framework to improve microaneurysm detection. Unlike
the well-known approach of considering the output of multiple classifiers, we propose a combination of
internal components of microaneurysm detectors, namely preprocessing methods and candidate
extractors. We have evaluated our approach for microaneurysm detection in an online competition,
where this algorithm is currently ranked as first, and also on two other databases.
Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopath...ITIIIndustries
With the improvement in IT industry, more and more application of computer software is introduced in teaching and learning. In this paper, we discuss the development process of such software. Diabetic Retinopathy is a common complication for diabetic patients. It may cause sight loss if not treated early. There are several stages of this disease. Fundus imagery is required to identify the stage and severity of the disease. Due to the lack of proper dataset of the fundus images and proper annotation, it is very difficult to perform research on this topic. Moreover, medical students are often facing difficulty with identifying the diseases in later stage of their practice as they may not have seen a sample of all of the stages of Diabetic Retinopathy problems. To mitigate the problem, we have collected fundus images from different geographic area of Bangladesh and designed an annotation software to store information about the patient, the infection level and their locations in the images. Sometimes, it is difficult to select all appropriate pixels of the infected region. To resolve the issue, we have introduced a K nearest neighbor (KNN) based technique to accurately select the region of interest (ROI). Once an expert (ophthalmologist) has annotated the images, the software can be used by the students for learning.
A Novel Advanced Approach Using Morphological Image Processing Technique for ...CSCJournals
Diabetic retinopathy (DR) is a common complication of diabetes mellitus and can lead to irreversible blindness. To date, DR is the leading cause of blindness and visual impairment among working adults globally. However, this blindness can be prevented if DR is detected early. Diabetes mellitus slowly affects the retina by damaging retinal blood vessels and leading to microaneurysms. The retinal images give detailed information about the health status of the visual system. Analysis of retinal image is important for an understanding of the stages of Diabetic retinopathy. Microaneurysms observed that appear in retina images, usually, the initial visible sign of DR, if detected early and properly treated can prevent DR complications, including blindness. In this research work, an advanced image modal enhancement comprises of a Contrast Limited Adaptive Histogram Equalization (CLAHE), through morphological image, processing technique with final extraction algorithm is proposed. CLAHE is responsible for the detection, and removal of the retinal optical disk. While the microaneurysm initial indicators are detected by using morphological image processing techniques. The extensive evaluation of the proposed advanced model conducted for microaneurysm detection depicts all stages of DR with an increase in the number of data set related to noise in the image. The microaneurysms noise is associated with stage of retina diseases as well as its early possible diagnosis. Evaluation is also conducted against the proposed model to measure its performance in terms of accuracy, sensitivity as well as specificity in real-time. The results show the test image attained 99.7% accuracy for a real-time database that is better compared with anty colony-based method. A sensitivity of 81% with a specificity of 90% was achieved for the detection of microaneurysms for the e-optha database. The detection of several microaneurysms correlates with stages of DR that prove an analysis of detecting its different stages. As well as it reaches our goal of early detection of DR with high performance in accuracy.
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
Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fu...IJAAS Team
In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE OF DIABETIC RETINOPATHYijabjournal
The aim of this paper is to detect exudates from the digital fundus images and provide information about Non Proliferative Diabetic Retinopathy. Diabetic retinopathy is very complicated disease that occurs when the retinal blood vessels changes. Exudates are the first sign of the diabetic retinopathy which cause blindness. So it is very important to find out these exudates in fundus image. In this paper we have proposed a method which is used for segmentation of optic disc and exudates. Morphological operations are used for detection of exudates. Before this operation we are applying Contrast Limited Adaptive Histogram Equalization technique. The results are compared with the standard database.
Review of methods for diabetic retinopathy detection and severity classificationeSAT 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
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.
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
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.
Normal vision means attaining 20/20 on a routine eye exam ie, one can read 3/8-inch letters at 20 feet. Approximately 285 million people worldwide cannot pass this test without correcting their vision. Sight problems range from normal to moderate or severe visual impairment. Thirty-nine million people are blind and ~90% of visually impaired people live in low-income settings. This presentation digs into the details and current treatments. This information is for educational purposes only and all medical cases should be discussed with licensed healthcare providers.
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
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
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
Early Detection of High Blood Pressure and Diabetic Retinopathy on Retinal Fu...IJAAS Team
In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.
EXUDATES DETECTION FROM DIGITAL FUNDUS IMAGE OF DIABETIC RETINOPATHYijabjournal
The aim of this paper is to detect exudates from the digital fundus images and provide information about Non Proliferative Diabetic Retinopathy. Diabetic retinopathy is very complicated disease that occurs when the retinal blood vessels changes. Exudates are the first sign of the diabetic retinopathy which cause blindness. So it is very important to find out these exudates in fundus image. In this paper we have proposed a method which is used for segmentation of optic disc and exudates. Morphological operations are used for detection of exudates. Before this operation we are applying Contrast Limited Adaptive Histogram Equalization technique. The results are compared with the standard database.
Review of methods for diabetic retinopathy detection and severity classificationeSAT 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
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.
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
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.
Normal vision means attaining 20/20 on a routine eye exam ie, one can read 3/8-inch letters at 20 feet. Approximately 285 million people worldwide cannot pass this test without correcting their vision. Sight problems range from normal to moderate or severe visual impairment. Thirty-nine million people are blind and ~90% of visually impaired people live in low-income settings. This presentation digs into the details and current treatments. This information is for educational purposes only and all medical cases should be discussed with licensed healthcare providers.
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
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
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
Diabetes is a chronic disease that is reaching epidemic proportions worldwide. There are currently more than
190 million people with diabetes worldwide. The World Health Organization (WHO) estimates that this will rise
to 221 million by the year 2010, largely due to population growth, ageing, urbanization and a sedentary lifestyle.
Diabetes is currently the fourth main cause of death in most developed countries. In Singapore, the prevalence
of diabetes in our population is 8.2% according to the 2004 National Health Survey. This is expected to grow as
our population age.
Diabetic Retinopathy, if not well managed and controlled, can progress steadily to devastating
complications like blindness. At present, various analyses on complicated interaction between hereditary and
environmental factors are being undertaken regarding the onset of diabetes. The development of diabetic
complication has become a major concern regarding the prognosis of diabetic patients.
Diabetes Retinopathy is one of the most common diseases that people get affected by over the years. By doing
this paper, we hope to detect the stages of Diabetic Retinopathy as early as possible so as to prevent and cure
more Singaporeans from falling prey to this disease
Detection of Diabetic Retinopathy using Kirsch Edge Detection and Watershed T...IJARIIT
Diabetic retinopathy (DR) is a common retinal complication associated with diabetics. A complication of diabetes is that it can also affect various parts of the body. When the small blood vessels have a high level of glucose in the retina, the vision will be blurred and can cause blindness eventually, which is known as diabetic retinopathy. However, if symptoms are identified in the early stage then proper treatment can be provided to prevent blindness. Usually the retinal images obtained from the fundus camera are examined directly and diagnosed. Due to this certain abnormalities due to diabetic retinopathy are not directly visible through the naked eye .Hence by using the image processing techniques these abnormalities can be extracted accurately and required treatments and precautions can be taken. And this also reduces the time for the ophthalmologists to detect the disease and give accurate treatments.
Detection of Diabetic Retinopathy in Retinal Image Early Identification using...ijtsrd
Diabetic Retinopathy, the most common reason of vision loss, is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since Diabetic Retinopathy is a silent disease that may cause no symptoms or only mild vision problems, annual eye exams are crucial for early detection to improve the chances of effective treatment where fundus cameras are used to capture the retinal images. However, fundus cameras are too big and heavy to be transported easily and too costly to be purchased by every health clinic, so fundus cameras are an inconvenient tool for widespread screening. Recent technological developments have enabled using smartphones in designing small sized, low power, and affordable retinal imaging systems to perform Diabetic Retinopathy screening and automated Diabetic Retinopathy detection using machine learning and image processing methods. However, Diabetic Retinopathy detection accuracy depends on the image quality and it is negatively affected by several factors such as Field of View. Since smartphone based retinal imaging systems have much more compact designs than the traditional fundus cameras, the retina images captured are likely to be low quality with smaller Field of View As a result, the smartphone based retina imaging systems can be used as an alternative to the direct ophthalmoscope once it tested in the clinical settings. However, the Field of View of the smartphone based retina imaging systems plays an important role in determining the automatic Diabetic Retinopathy detection accuracy. M. Mukesh Krishnan | J. Diofrin | M. Vadivel "Detection of Diabetic Retinopathy in Retinal Image Early Identification using Deep CNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd55047.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/other/55047/detection-of-diabetic-retinopathy-in-retinal-image-early-identification-using-deep-cnn/m-mukesh-krishnan
Retinal Complications in Indians with Type 2 Diabeticsijtsrd
Diabetic retinopathy is the disease that affects diabetics the most often DR . The duration of the disease, ineffective control of blood sugar, and the presence of hypertensive are the key causes. Yet, large inter individual differences in risk indicate that other factors, like as genetic inheritance or insulin variability, are critical in explaining susceptibility to DR development. It is also important to recognise that DR can predict both microvascular and macrovascular issues independently. Hence, DR needs to be factored in when determining the cardiovascular risk of a diabetic. Even if dementia is becoming more prevalent in people with type 2 diabetes, evaluating retina neurodegeneration could help in spotting those at risk. The therapeutic implications of DR awareness in the assessment of a diabetic patient cannot be overstated. It follows that DR may worsen despite a rapid decrease in blood sugar. To wrap up, this article provides a critical evaluation of DRs function within entire care of diabetic patients. Dr. Dhruv Kundu "Retinal Complications in Indians with Type 2 Diabetics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-2 , April 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd53983.pdf Paper URL: https://www.ijtsrd.com.com/medicine/other/53983/retinal-complications-in-indians-with-type-2-diabetics/dr-dhruv-kundu
Automated Screening of Diabetic Retinopathy Using Image Processingiosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
How AI Enhances & Accelerates Diabetic Retinopathy DetectionCognizant
To enable earlier and quicker diagnosis of diabetic retinopathy (DR), Cognizant has built a system based on AI and deep learning - a convolutional neural network - that analyzes many thousands of fundus images and delivers accurate assessments of eye-disease damage.
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%
Diabetic retinopathy is a complication of diabetes causing progressive damage to the retina, located at the back of the
eye, potentially leading to clouded vision or blindness. Disease signs may be visualized by Optical Coherence Tomography
(OCT) and include formation of new and weaker blood vessels, fluid accumulation, exudates and changes to Retinal Vascular
Geometry (RVG). Presence of these indicators can provide information as to the stage of the disease. Image-processing
strategies are applied for the automated detection, segmentation, extraction, classification toward likelihood estimation of
progression of diabetic retinopathy to visual biomarkers present in OCT, using time-sequenced data in the early stages of the
disease. Gabor and Savitsky-Golay filtering enables extraction of the vessel map and fuzzy control for segmentation of hard
exudates. Feature data are extracted using bounding boxes, vector map and connected component methodology for binary
decision tree classifier construction, training and testing. Feature values comprising classifier nodes include: exudate features
of compactness, area, convexity and form factor, in addition to vessel features: width, elongation, bifurcation angles, form
factor and solidity. Classifier accuracy is 93.3%, with 6.7% misclassification and 0% false-negative classification. Automated
image processing of diabetic retinopathy is achieved with high classification accuracy for the extraction of vessel map and
hard exudate biomarkers from OCT. Application of smoothing algorithms and removal of vessel map shadows may further
improve classification accuracy.
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.
Global Medical Cures™ | DIABETIC RETINOPATHY
DISCLAIMER-
Global Medical Cures™ does not offer any medical advice, diagnosis, treatment or recommendations. Only your healthcare provider/physician can offer you information and recommendations for you to decide about your healthcare choices.
Extraction of Features from Fundus Images for Diabetes Detectionpaperpublications3
Abstract: Diabetes is a quickly increasing worldwide problem which is produced by defective organic process of glucose secretion that produces long-term dis-function and harmful for different organs. The major problem of diabetes is diabetic retinopathy (DR), which are vascular diseases affecting the retina due to long time diabetes. It can produce sudden vision loss due to DR. So we need to develop the system to examine the retinal images for obtain important features of diabetic retinopathy by using the image processing techniques. First the entire image is segmented. From that segmented regions, we can check varying changes in blood vessels and different features for e.g. exudates, microaneurysms, and also a set of features such as color, size, edge and texture which can be used as part of an automatic diabetes recognition system.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
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And...
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Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
C018121117
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 18, Issue 1, Ver. II (Jan – Feb. 2016), PP 11-17
www.iosrjournals.org
DOI: 10.9790/0661-18111117 www.iosrjournals.org 11 | Page
Automatic Detection of Microaneurysms in digital fundus images
using LRE
Raju Sahebrao Maher
Research Student
Department of Computer Science & IT,Dr. Babasaheb
Ambedkar Marathwada University, Aurangabad,
Maharashtra, India
rajputraj17@gmail.com
Mukta Dhopeshwarkar
Assistant Professor
Department of Computer Science & IT,Dr. Babasaheb
Ambedkar Marathwada University, Aurangabad,
Maharashtra, India
Abstract: Diabetic retinopathy is a complication of diabetes and a leading cause of blindness in the world. It
occurs when diabetes damages the tiny blood vessels inside the retina. The main two types of diabetic
retinopathy the first are non-proliferate diabetes retinopathy (NPDR) and second are proliferate diabetes
retinopathy (PDR). Images analysis by trained individuals, which can be a very costly and time consuming task
due to the large diabetic population. The increasing number of DR cases world-wide. Therefore, demands to the
development of an automated detection system for reduce the work load of ophthalmologists in diagnosis the
disease.
This research proposed is developed computer based system for the automatically detection of diabetic
retinopathy in the fundus images using Support vector classification (SVM) algorithm. The proposed
methodology, using DIARETDB1 in which have total 89 images, achieves an accuracy of 95.38%, sensitivity of
94 % and a specificity of 94.7%.
Keywords: Diabetic retinopathy, Image processing, screening, Microaneurysm, Fundus image analysis.
I. Introduction
Diabetes is a disorder of sugar metabolism caused by an impairment of insulin raised levels
of glucose in the blood. High levels of glucose in the blood can damage the vessels that supply blood
to vital organs. Diabetic Retinopathy (DR) is the resultant disorder affecting the retinal vasculature,
leading to progressive retinal damage that can loss to vision and can occur blindness [2]. DR is
recognized as the leading cause of blindness in the working-age population [3]. The problem is
increasing in its scale, with diabetes having been identified as a significant growing global public
health problem [4]; in fact in the United Kingdom three million people are estimated to have diabetes
in future is may be expected to double in the next 15-30 years. Diabetic Patients are required to
attend regular eye screening needed to appointments in which DR, a retinal disorder, can be assessed.
From these appointments digital retinal images are captured, and these then undergo various stages
of analysis by trained individuals. This can be a very time consuming and costly task due to the large
number of diabetic patients. Therefore this is a field that would greatly benefit from the introduction
of automated detection systems [1]. Not only would its implementation be more cost effective, but
the screening program and the National Health organization (NHO) would benefit in numerous other
ways. Results would be produced more quickly, thus allowing patients to receive results as soon as
possible hence minimizing anxiety and also ensuring referrals to the hospital eye service. Another
benefit arises from the fact that human graders are subjective and can also become fatigued, whereas
an automated system would provide consistent objective results. In medical imaging the quality of
the image acquisition and the image interpretation determines the accuracy of diagnosis. Desktop
have a huge impact on the of medical images acquisition. They perform multipronged functions like
controlling imaging, performing reconstruction, post-processing of the image data and storing the
scans. In contrast, the role of computers in the interpretation of medical images has so far been
limited.
This paper describes components an automatic system that can aid in the detection of diabetic
retinopathy. DR is an eye disease and its causes vision loss, if left undiagnosed at the initial stage. In
the world number of diabetes affected people is increasing, the need to increase automated detection
methods for diabetic retinopathy. To automatically detect diabetic retinopathy, a computer has to
interpret and analyze digital images of the retina.
2. Automatic Detection of Microaneurysms in digital fundus images using LRE
DOI: 10.9790/0661-18111117 www.iosrjournals.org 12 | Page
In this work developed to assist ophthalmologist’s diagnosis by providing second opinion and also
functions as an automatic tool for the mass screening of diabetic retinopathy. Colour fundus images
are used by ophthalmologists to study eye diseases like diabetic retinopathy. Extraction of the normal
features like optic disk, fovea and blood vessels; and abnormal features like exudates, cottonwool
spots, microaneurysms and hemorrhages from colour fundus images are used in fundus image
analysis system for comprehensive analysis and grading of diabetic retinopathy[5]. This CAD system
also provides the spatial distribution of abnormalities based on fovea such that an ophthalmologist
can make a detailed diagnosis. This introductory Chapter presents some background information on
the anatomy of the eye, diabetic retinopathy, and diabetic retinopathy screening [6].
1.1. Diabetic Retinopathy
Diabetic retinopathy is the prime cause of vision loss amongst the working age population
of the developing and the developed countries. Diabetic patients are 25 times more probable to
become blind than non-diabetic patients [1]. Diabetic retinopathy is a complication of diabetes to the
retina. Both the forms of diabetes i.e. diabetes mellitus and diabetes insepidous, leads to diabetic
retinopathy eventually after some time. It is a very asymptomatic disease in the early stages and it
could lead to permanent vision loss if untreated for long time. The problem here is the patients may
not know about it until it reaches advanced stages. Once it reaches advanced stages vision loss
becomes inevitable. As diabetic retinopathy is the third major cause of blindness particularly in
India, there is an immediate requirement to develop efficient diagnosis methods for this problem.
The age of onset and the duration of the diabetes are the two most important issues that determine the
incidence of diabetic retinopathy. Among the patients below the age of 30 years, when first
diagnosed with diabetes, the prevalence is 17% during the first 5 years. This increases to 97% after
15 years of diabetes [2]. Amongst the patients above the age of 30 years at the onset of diabetes, 20%
have showed signs of retinopathyimmediately after presentation and this increased to 78% after 15
years of diabetes [3].
This results in retinal hemorrhages either superficially or in deeper layers of the retina (Fig. 1(a)). As
the retinal blood vessels become moredamaged and permeable,
(a) (b)
Fig.1. (a) non diabetic retinopathy Vision (b) With Retinopathy vision [7].
Their number will increase. Hemorrhages look either as small red dots or blots identical to
microaneurysms or as larger flame-shaped hemorrhages.The vessels besides leaking blood also cause
the leakage of lipids and proteins paving way for the appearance of small bright dots called exudates
(Fig. 1. (b)). They are seen on the retina as typical bright, reflective white or cream coloured lesions
that indicate increased blood vessel permeability and an allied risk of retinal edema. If this takes
place on the macula region vision may be lost.
1.2. Types of Diabetic Retinopathy
DR can be broadly classified as non-proliferative DR (NPDR) and proliferative DR (PDR).
1.2.1. Non-Proliferative Diabetic Retinopathy
Mild NPDR:at least one microaneurysm with or without the presence of retinal haemorrhages, hard
exudates, cotton wool spots or venous loops. Approximately 40% of people with diabetes have at least mild
signs of diabetic retinopathy.
Moderate NPDR:numerous microaneurysms and retinal haemorrhages are present. A limited amount
and cotton wool spots of venous beading can also be seen. 16% of the patients with moderate NPDR will
develop PDR within one year.
3. Automatic Detection of Microaneurysms in digital fundus images using LRE
DOI: 10.9790/0661-18111117 www.iosrjournals.org 13 | Page
Severe NPDR:is characterized by any one of the following characteristics: (i) numerous haemorrhages
and microaneurysms in quadrants of the retina. (ii) venous beading in 2 or more quadrants. (iii) Intraretinal
microvascular abnormalities in at least 1 quadrant. Severe NPDR carries a 50% chance of progression to PDR
within one year.
1.2.2. Proliferative Diabetic Retinopathy
PDR is the advanced stage in this stage signals sent by the retina for nourishment trigger for
the growth of new blood vessels. This blood vessels do not cause symptoms or vision loss. But, their
walls are thin and fragile, this indications to a high risk that they leak blood. This leaked blood
contaminates the vitreous gel and this causes severe vision loss and even blindness. About 3% of
people, may experience severe visual loss.
Fig.2. New blood vessels growing on the retinal surface
The new vessel growth in diabetes only occurs in the retina, nowhere else in the body. When a retina
becomes damaged by a higher than normal sugar, over many years, it seems to release special growth hormones.
1.3. The objectives of this research work are
To Segment Retinal Blood Vessel Tree
To Detect Red Lesions such as Microaneurysms.
The outcomes of these two components are used in diagnosis of diabetic retinopathy component. The
fundus image analysis system grades diabetic retinopathy and macular edema based on the detection of these
lesions and this system also provides the spatial distribution of abnormalities based on fovea such that an
ophthalmologist can make a detailed diagnosis.
II. Literature Review
This chapter provides the details related to basics of medical domain, especially ophthalmology. The
main focus of this literature review has been to study in detail the various image processing techniques involved
in the field of retinal image analysis. This chapter also presents a detail literature survey of existing methods on
the automatic detection of anatomical structures in retina and current scenario of automatic diagnostic systems.
The extraction of features has also been documented in detail. The research field of retinal image analysis has
attracted a lot of interest in the last couple of decades, with the automated detection of Diabetic Retinopathy
(DR) having received a considerable share of this interest. Revolutionary detection is also an area that has
received significant interest. Landmarks consist of blood vessels, the optic disc and the fovea. This section will
start with a brief review of the automated segmentation of blood vessels. Most DR detection methodologies use
it as a requirement before identifying pathological entities
2.1 Blood Vessel Segmentation
Several studies were carried out on the segmentation of blood vessels in generally some of the main
attributes of vessels that are utilized in segmentation methods are their deep red colour, their contrast with the
background, and their gradient at vessel edges. however only a small number of them were associated to retinal
blood vessels. In order to review the methods proposed to segment vessels in retinal images, seven classes of
methods have been considered: matched filters, vessel tracking, morphological processing, region growing,
multiscale, supervised and adaptive thresholding approaches.
4. Automatic Detection of Microaneurysms in digital fundus images using LRE
DOI: 10.9790/0661-18111117 www.iosrjournals.org 14 | Page
2.2 Matched Filters
Matched filters as based on a correlation measure between the expected shape sought for and the
measured signal. The algorithm presented by Chaudhuri et al. [1] was based on directional 2D matched filter. To
enhance retinal vasculature a 2D matched filter kernel was designed to convolve with the original fundus image.
The kernel was rotated into either eight or twelve orientations to fit into blood vessels of various configurations.
A number of kernel shapes have been investigated. Gaussian kernels were used in [1-3]. Kernels based on lines
[4] and partial Gaussian kernels were also used [5].A number of approaches were also proposed to identify true
blood vessels from the matched filter response. A region based threshold probe was applied to the response of
matched filter to segment the vessel network [3]. An amplitude modified second order differential Gaussian
filter was proposed by. [6] to detect the vessel network at different scales that match their widths. This was
achieved by changing the amplitude, so that responses can be merged over scales. Local entropy based
thresholding was proposed by maher et al. [7].
2.3 Morphological Processing
To segment the blood vessels in a retinal image, mathematical morphology can be used since the
vessels were the patterns that exhibit morphological properties. Then returning to the original pre-processed
image, vessel enhancement was performed separately at different scales by using a modified top hat operator
(morphological operator) with a disc structural element of increasing size used to enhance vessels with different
widths, such as connectivity, linearity and curvature of vessels varying smoothly along the crest line. But
background patterns also fit to such a morphological description. In order to discriminate blood vessels from
other similar structures, cross - curvature evolution and linear filtering were employed by Klein et al. [8]. A two
step method was applied to segment vasculature by Purty et al. [9]. Firstly, mathematical morphology filtering
coupled with curvature evolution was utilized to enhance the blood vessels in fundus images.
2.4 Dark Lesion Detection
Microaneurysms and Hemorrhages are the red or dark lesions found in the retinal images.
Microaneurysms appear in the very early stages of diabetic retinopathy and hemorrhages appear in the
proliferative diabetic retinopathy stage. Hence, detection of former tells us to detect the disease at the earliest
and later tells whether diabetic retinopathy is in advanced stage or not. For this reason, the detection of these
two dark lesions is very important. Microaneurysm and hemorrhage counts are very good indicators of
progression of the disease. Several methods for detecting dark lesions were reported in the literature. The dark
lesion detection algorithm proposed by Kinyoun et al. [10] contains three stages: firstly, a set of correlation
filters were applied to extract candidate dark lesions. In the second stage, segmentation based on region growing
was applied to reject candidate dark lesions whose size does not fit in the pattern of dark lesion. Finally, three
tests i.e. a shape test, an intensity test and a test to eliminate the points that fall inside the blood vessels (only
lesions outside the vessels were considered) were used to find true dark lesions. A nonlinear curve with
brightness values of the HSV space was used to change the brightness of the fundus image [11].
III. Digital Image Database And Preprocessing
Fundus imaging has an important role in diabetes monitoring since occurrences of retinal abnormalities
are common and consequences serious. However, since the eye fundus seems to be sensitive to vascular
diseases, fundus imaging is considered as a candidate for non-invasive screening of diabetes. The success rate of
screening depends on accurate fundus image capturing and especially on accurate and reliable image processing
algorithms for detecting the abnormalities. Various algorithms have been proposed by many research groups for
this purpose. However, it is impossible to judge the accuracy and reliability of the approaches because of the
lack of commonly accepted and representative fundus image database and evaluation protocol.
One publicly retinal databases available called DIARETDB1 standard database is used for testing exudate
detection method. The details of these databases are as follows.
3.1. Diaretdb1 Database
The database consists of 89 colour fundus images of which 84 contain at least mild non-proliferative
signs of the diabetic retinopathy (see Figure 3.), and five are considered as normal which do not contain any
signs of the diabetic retinopathy according to all the experts participated in the evaluation. Images were
captured with the same 50 degree field-of-view digital fundus camera with varying imaging controlled by
the system in the Kuopio university hospital, Finland. The image ground truth provided along with the
database is based on expert selected findings related to the diabetic retinopathy and normal fundus structures.
Special software was used to inspect the fundus images and annotate the findings as hard exudates, hemorrhages
and microaneurysms.
5. Automatic Detection of Microaneurysms in digital fundus images using LRE
DOI: 10.9790/0661-18111117 www.iosrjournals.org 15 | Page
(a) (b)
Fig.3.(a)ExampleofabnormalfundusimagefromDIARETDB1database(b) Ground truth of hard exudates.
3.2. Pre-processing
Patient movement, poor focus, bad positioning, reflections, inadequate illumination can cause a
significant proportion of images to be of such poor quality as to interfere with analysis. In the retinal images
there can be variations caused by the factors including differences in cameras, illumination, acquisition angle
and retinal pigmentation.Pre-processing is to reduce this effect by enhancing the contrast and normalizing the
mean intensity. The objective of preprocessing is to attenuate the noise, to improve the contrast and to correct
the non-uniform illumination. The colour retinal images taken at screening programs are often poorly contrasted
and contain artifacts. A local contrast enhancement method [12] and Polynomial contrast enhancement is
applied to the intensity image to improve both the contrast of bright lesions and the overall colour saturation of
the retinal image. There can also be difference in the colour of the fundus due to retinal pigmentation among
different patients. These images are preprocessed before they can be subjected to anatomical and pathological
structure detection.
IV. Experimental Results And Discussion
Diabetic retinopathy is one of the prime causesof vision loss and blindness amongst the people of
developing and developed world and present in 30% of diabetic population [1]. Blindness from diabetic
retinopathy can be prevented but needs regular eye checks and control of blood sugar levels. Microaneurysms
such as Dark lesions and hemorrhages and bright lesions such as hard exudates and cottonwool spots are the
visible signs of diabetic retinopathy. Microaneurysms also known as red lesions are the first clinically
observable lesions indicating diabetic retinopathy. Therefore, their detection is very important for a diabetic
retinopathy screening system. The major challenges in dark lesion detection are: Segmentation of small Dark
lesions in the regions of low image contrast. The presence of bright pathologies. Normally bright lesions have
sharp edges. These sharp edges can be detected as false positives in the later stages. The principal objective in
this chapter is to develop a new method that can identify fundus images consisting microaneurysms and
hemorrhages with a very high sensitivity by segmenting all the possible microaneurysms, while avoiding false
responses near bright pathologies and other non-dark lesion structures. The proposed dark lesion detection
method comprises of three steps. First, the green channel of each colour retinal image is extracted and is
preprocessed using polynomial contrast enhancement. Next, the candidate objects representing microaneurysms
are segmented.
4.1. Support Vector Machine
SVM are a set of supervised learning tools applied for data classification and regression. SVM model
maps the training samples that are the points in features space into different categories which are clearly
separated with the widest gap in between them. The testing samples are mapped to the same feature space and
classified as belonging to any of the classes. SVM constructs an optimal hyper plane that would maximize the
margin of separation between the classes. The feature vectors that lie close to the margin are called the support
vectors. Figure 4.Depicts the SVM classifier with the optimum hyperplane. A binary SVM finds an optimum
hyper plane which separates the feature vectors of the two classes with largest margin from the hyper plane.
6. Automatic Detection of Microaneurysms in digital fundus images using LRE
DOI: 10.9790/0661-18111117 www.iosrjournals.org 16 | Page
4.2. Candidate Microaneurysm Detection System
Microaneurysms detection is very important, because these structures constitute the earliest
recognizable feature of DR. Here, the potential microaneurysms are segmented from the preprocessed images.
The objects thus segmented are called candidate objects.
4.2.1 Mathematical Morphology Based Method
After the image is preprocessed, the candidate microaneurysms are segmented by separating circular,
non-connected microaneurysms from the blood vessels. Morphological top-hat transformation is applied for this
purpose. The top-hat transformation is based on morphologically opening an image using a linear structuring
element. Twelve rotated structuring elements are applied with radial resolution of the structuring element length
should be chosen such that it must be larger than the largest microaneurysms present in the set.
(a) (b) (c)
Fig.4 (a) Microaneurysm on Colour Fundus Image (b) Micro-aneurysms on Colour Fundus Image (c) Detected
Microaneurysms using Morphology Based Method.
The drawback of candidate extraction technique based on mathematical morphology is that any
exudates that is bigger than the linear structuring element cannot be identified. If the length of the structuring
element is increased to extract larger objects, then more spurious candidate exudates objects will be detected on
blood vessels as the segmentation of blood vessels deteriorates. A pixel classification based method can be used
to eliminate this drawback.
The proposed detection methods are tested and evaluated on DIARETDB1, a publicly available
database of colored fundus images and corresponding ground truth images.
Table 1: Performance of proposed method for hard exudates detection
Database Total
images
Non-
microaneurysmimag
es
No. of images with
microaneurys
Sensitivity
(%)
Specificity
(%)
Diaretdb1 89 32 57 94% 94.7%
Lesion based evaluation and image based evaluation are employed to measure the accuracy of the
proposed detection method at the pixel level.In example the green component, of the RGB fundus image, was
chosen to obtain the microaneurysms. Similar to the exudates detection algorithm, first the prominent structures
within retina images, Such as blood vessel tree and optic disc are to be removed. After that a sophisticated
sequence of image processing algorithms was used to determine the areas within the fundus images to get
microaneurysms.
V. Conclusion
In this paper an automated method to detect microaneurysm in digital fundus images. A new candidate
microaneurysm detection scheme based on matched filtering and local relative entropy is proposed. The
performance of this microaneurysm detection method is compared with mathematical morphology based
microaneurysm detection method. A proposed detection scheme that combines detection methods is tested as
well. The results of the method on a per image basis show that the proposed detection scheme achieved an
accuracy of 95.38%, sensitivity of 94% combined with 94.7% specificity.
7. Automatic Detection of Microaneurysms in digital fundus images using LRE
DOI: 10.9790/0661-18111117 www.iosrjournals.org 17 | Page
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