This document presents a method for detecting exudates caused by diabetic retinopathy in fundus retinal images using fuzzy K-means clustering and a neural network. It begins with background on the anatomy of the eye, retinal images, diabetes, and diabetic retinopathy. Exudates are described as lipid and protein accumulations in the retina. The method involves pre-processing images using histogram equalization, fuzzy K-means clustering to identify exudates, and a backpropagation neural network. Results on normal and abnormal images show the method can efficiently detect exudates within a short time compared to other techniques. The accuracy of diagnosing retinal diseases from images is improved.
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.
1. Diabetic macular edema (DME) is the most common cause of visual impairment in patients with diabetes and can be focal or diffuse.
2. Clinically significant macular edema is defined by retinal thickening within 500 μm of the macula or hard exudates associated with thickening.
3. Optical coherence tomography and fluorescein angiography are used to diagnose and characterize DME by detecting retinal thickening and leakage patterns.
This document provides an overview of diabetic macular edema (DME) and its treatment. It discusses the results of major clinical trials that established standards of care. The Early Treatment Diabetic Retinopathy Study (ETDRS) first demonstrated that focal laser photocoagulation can reduce vision loss from DME. However, many patients still lose vision with laser alone. Recent studies show anti-VEGF agents like ranibizumab are now the standard treatment, providing better outcomes than laser. Clinical trials found ranibizumab improves vision in DME and fewer injections are needed over time using PRN regimens. While laser remains an option, anti-VEGF agents lead to greater vision gains and have replaced laser
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
This document discusses two eye conditions: blepharospasm and xerophthalmia.
Blepharospasm is a focal dystonia causing involuntary eyelid closure. It is most common in those aged 50-60 and more frequent in women. Treatment includes botulinum toxin injections into the eyelids, which provide temporary relief of symptoms requiring repeat injections every 3 months.
Xerophthalmia results from vitamin A deficiency and can cause night blindness, dry eyes, corneal ulcers or scarring. Its stages are classified based on symptoms. Treatment focuses on reducing the underlying cause through dietary changes.
GENERAL INFORMATION ABOUT DIABETIC MACULAR EDEMA WITH 2 PATIENT CASES, TREATED WITH 2 DIFFERENT TREATMENT TECHNIQUES.
CLASSIFICATION (CSME)
RISK FACTORS
CAUSES
SIGNS AND SYMPTOMS
MANAGEMENT AND TREATMENT OPTIONS
DIAGNISTIC TESTS, BLOOD AND URINE TEST
SCORING SYSTEM
PATHOLOGY
DIFFERENTIAL DIAGNOSIS
PROGNOSIS
EPIDEMIOLOGY
DESCRIPTION OF 2 CASES, THEIR DIAGNOSTIC RESULT AND DETAILS ABOUT TREATMENTS PERFORMED.
Macular degeneration treatments - an update for orthoptists TheEyeExpert
This document summarizes treatment options for macular degeneration. It begins with an overview of retinal anatomy and causes of macular degeneration related to light damage and age-related impaired recycling in the retina. It then discusses current treatments for wet macular degeneration which involve injections of anti-VEGF drugs like Lucentis or Eylea every 1-3 months on average. Future developments may reduce injection frequency through radiation treatments or more potent drugs. Dry macular degeneration has fewer treatment options and focuses on low vision aids and nutritional supplements. The document reviews imaging techniques, costs of treatment, injection procedures, and other macular conditions.
IRJET- Detection of Diabetic Retinopathy using Convolutional Neural NetworkIRJET Journal
This document describes research using a convolutional neural network to detect diabetic retinopathy from fundus images. The researchers trained a CNN model on a dataset of over 35,000 fundus images to classify images into five stages of diabetic retinopathy severity. The CNN model extracts features from input fundus images and uses activation functions and optimization algorithms to output a classification. The classification along with patient details will generate a standardized report on diabetic retinopathy detection and diagnosis.
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.
1. Diabetic macular edema (DME) is the most common cause of visual impairment in patients with diabetes and can be focal or diffuse.
2. Clinically significant macular edema is defined by retinal thickening within 500 μm of the macula or hard exudates associated with thickening.
3. Optical coherence tomography and fluorescein angiography are used to diagnose and characterize DME by detecting retinal thickening and leakage patterns.
This document provides an overview of diabetic macular edema (DME) and its treatment. It discusses the results of major clinical trials that established standards of care. The Early Treatment Diabetic Retinopathy Study (ETDRS) first demonstrated that focal laser photocoagulation can reduce vision loss from DME. However, many patients still lose vision with laser alone. Recent studies show anti-VEGF agents like ranibizumab are now the standard treatment, providing better outcomes than laser. Clinical trials found ranibizumab improves vision in DME and fewer injections are needed over time using PRN regimens. While laser remains an option, anti-VEGF agents lead to greater vision gains and have replaced laser
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
This document discusses two eye conditions: blepharospasm and xerophthalmia.
Blepharospasm is a focal dystonia causing involuntary eyelid closure. It is most common in those aged 50-60 and more frequent in women. Treatment includes botulinum toxin injections into the eyelids, which provide temporary relief of symptoms requiring repeat injections every 3 months.
Xerophthalmia results from vitamin A deficiency and can cause night blindness, dry eyes, corneal ulcers or scarring. Its stages are classified based on symptoms. Treatment focuses on reducing the underlying cause through dietary changes.
GENERAL INFORMATION ABOUT DIABETIC MACULAR EDEMA WITH 2 PATIENT CASES, TREATED WITH 2 DIFFERENT TREATMENT TECHNIQUES.
CLASSIFICATION (CSME)
RISK FACTORS
CAUSES
SIGNS AND SYMPTOMS
MANAGEMENT AND TREATMENT OPTIONS
DIAGNISTIC TESTS, BLOOD AND URINE TEST
SCORING SYSTEM
PATHOLOGY
DIFFERENTIAL DIAGNOSIS
PROGNOSIS
EPIDEMIOLOGY
DESCRIPTION OF 2 CASES, THEIR DIAGNOSTIC RESULT AND DETAILS ABOUT TREATMENTS PERFORMED.
Macular degeneration treatments - an update for orthoptists TheEyeExpert
This document summarizes treatment options for macular degeneration. It begins with an overview of retinal anatomy and causes of macular degeneration related to light damage and age-related impaired recycling in the retina. It then discusses current treatments for wet macular degeneration which involve injections of anti-VEGF drugs like Lucentis or Eylea every 1-3 months on average. Future developments may reduce injection frequency through radiation treatments or more potent drugs. Dry macular degeneration has fewer treatment options and focuses on low vision aids and nutritional supplements. The document reviews imaging techniques, costs of treatment, injection procedures, and other macular conditions.
IRJET- Detection of Diabetic Retinopathy using Convolutional Neural NetworkIRJET Journal
This document describes research using a convolutional neural network to detect diabetic retinopathy from fundus images. The researchers trained a CNN model on a dataset of over 35,000 fundus images to classify images into five stages of diabetic retinopathy severity. The CNN model extracts features from input fundus images and uses activation functions and optimization algorithms to output a classification. The classification along with patient details will generate a standardized report on diabetic retinopathy detection and diagnosis.
1. The document discusses recent treatments for diabetic macular edema (DME), including laser therapy, anti-VEGF drugs like ranibizumab and bevacizumab, and steroids.
2. It also covers screening guidelines, lifestyle management to control diabetes and risks factors, and potential future therapies like implants for sustained drug delivery.
3. Surgery options like vitrectomy are discussed when DME is severe, along with postsurgical follow up schedules.
This document provides an overview of diabetic retinopathy diagnosis through the analysis of retinal images. It discusses the aims of identifying patients with different stages of diabetic retinopathy. The stages of diabetic retinopathy and associated symptoms are defined. Pre-processing steps like color conversion, filtering and segmentation are described. A proposed methodology includes blood vessel and lesion detection through morphological operations, texture analysis, feature extraction and classification. Results of optic disc detection, blood vessel segmentation and texture analysis are shown. The conclusion discusses developing more accurate detection techniques and extracting smaller blood vessels to aid in diagnosis.
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
Optical Coherence Tomography in Multiple Sclerosisneurophq8
OCT is a non-invasive technology used in ophthalmology to assess retinal diseases and glaucoma. In recent years , OCT has been used to assess axonal loss and neurodegeneration in MS. This presentation will highlight the main uses of the OCT in MS and review of the literature.
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.
Eylea (aflibercept) is a treatment for wet age-related macular degeneration (AMD) and central retinal vein occlusion (CRVO). Phase 3 clinical trials found aflibercept to be effective in improving vision for both conditions compared to controls. For wet AMD, VIEW 1 and VIEW 2 trials found aflibercept non-inferior to ranibizumab. For CRVO, Copernicus found aflibercept superior to sham injections and Galileo found it superior to sham injections. Cost analysis found aflibercept increases health plan spending but is cost-effective compared to alternatives. The committee recommends keeping aflibercept non-preferred due to cost but reevaluating as more data is published
Optic neuritis is an inflammation of the optic nerve that can be divided into 3 types based on appearance: papillitis, neuroretinitis, and retrobulbar neuritis. Papillitis is most common in children and involves edema and hyperemia of the optic disc. Neuroretinitis also involves the retinal nerve fiber layer and macula. Retrobulbar neuritis most commonly affects adults and the optic disc may appear normal initially. Causes include demyelinating disorders like multiple sclerosis, infections, immune disorders, and metabolic conditions. Treatment involves steroids like intravenous methylprednisolone to hasten recovery of vision, with the goal of improving outcomes and delaying development of multiple sclerosis
1. Diabetic macular edema (DME) is a common retinal disorder where fluid collects in the macula due to leaky blood vessels, causing it to swell and blurring vision.
2. DME is diagnosed through a dilated eye exam, optical coherence tomography, and sometimes fluorescein angiography.
3. Treatment involves controlling diabetes and other risk factors, laser therapy to seal leaky blood vessels, and injections of anti-VEGF drugs which reduce new blood vessel growth and leakage.
Diabetic retinopathy is damage to the blood vessels of the retina due to diabetes. A study compared the standard treatment of pan-retinal photocoagulation (PRP) to initial treatment with intravitreal anti-VEGF injections (ranibizumab) to delay or prevent the need for PRP. At 2 years, vision was maintained or improved in the ranibizumab group while vision remained unchanged in the PRP group. The study suggests initial treatment of proliferative diabetic retinopathy with anti-VEGF injections like ranibizumab may be as effective as immediate PRP treatment and could help delay or reduce the need for destructive PRP therapy.
This document discusses diabetic retinopathy and its management. It begins by stating that diabetic retinopathy is the leading cause of vision impairment worldwide. It then describes the clinical presentation and stages of diabetic retinopathy from mild non-proliferative to proliferative, as well as diabetic macular edema. The management section discusses preventing and treating diabetic retinopathy through controlling blood sugar and blood pressure systematically, as well as local treatments like pan-retinal photocoagulation laser, anti-VEGF injections, and vitrectomy surgery. It emphasizes that timely screening and treatment according to guidelines can reduce the risk of severe vision loss to less than 5%.
This document provides guidelines for screening, monitoring, classifying severity, and treating diabetic macular edema (DME). It recommends annual screening of diabetic patients aged 15+ for retinopathy and treating any sight-threatening cases found. For DME treatment, it discusses traditional laser photocoagulation as well as newer options like intravitreal corticosteroids and anti-VEGF drugs. Intravitreal injections of anti-VEGF agents are considered first-line therapy for center-involving DME, with laser as an option for non-center cases or if thickening persists after anti-VEGF treatment. Strict control of modifiable risk factors like glycemia, blood pressure, and lipids can also help prevent
This document discusses macular edema in diabetic retinopathy and its treatment. It explains that macular edema is caused by chronic hyperactivity of the polyol pathway and increased vascular endothelial growth factor, which leads to increased vascular permeability. Laser photocoagulation and intravitreal anti-VEGF injections are effective treatments that work by reducing hypoxia and VEGF levels in the retina. Several studies found that treatments with drugs like ranibizumab and aflibercept were more effective at improving vision outcomes than laser alone in patients with diabetic macular edema.
Age related macular degeneration from Optometrist Point of ViewAnis Suzanna Mohamad
Age-related macular degeneration (ARMD) is a degenerative eye disease that affects the macula and can cause vision loss, with risk factors including aging, smoking, and family history. It has early and late stages, with the late stage including dry ARMD characterized by retinal pigment epithelium and photoreceptor atrophy, and wet ARMD involving abnormal blood vessel growth beneath the retina. Treatment options depend on the stage and type of ARMD, with anti-VEGF injections and photodynamic therapy used for wet ARMD to prevent further vision loss.
Retinal diseases can affect vision and cause blindness. Common retinal conditions include floaters, macular degeneration, diabetic eye disease, retinal detachment, and retinitis pigmentosa. Floaters are spots in vision caused by age-related changes in the vitreous jelly inside the eyes. Macular degeneration is deterioration of the macula and is diagnosed as dry or wet. Diabetics are susceptible to retinal damage from conditions like diabetic eye disease. Retinal detachment occurs when fluid builds behind the retina, causing separation. Retinitis pigmentosa describes genetic conditions that cause retinal degeneration over time.
Slides include
Basic anatomy of optic nerves
Background & epidemiology of optic neuritis
Classification of optic neuritis
Clinical features
Investigations
Diagnosis
Differential diagnosis
Managements
Prognosis
Central serous chorioretinopathy (CSR) is characterized by localized serous retinal detachment caused by leakage from the choroid through hyperpermeable areas of the retinal pigment epithelium (RPE). It typically affects males aged 25-55 and can be associated with stress, pregnancy, corticosteroid use, and obstructive sleep apnea. Patients may experience blurred vision, metamorphopsia, and micropsia. Diagnostic testing includes OCT, FA, and ICGA to detect choroidal vascular abnormalities and RPE leaks. While many cases resolve spontaneously, recurrent or chronic CSR can cause permanent vision loss and may be treated with corticosteroid cessation, laser, photodynamic
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.
Case Report and Clinical Findings of Central Serous RetinopathyDan Mulder
Central Serous Retinopathy (CSR) is a retinal disease caused by a serous detachment of the retina. The case report details examination findings and diagnostic testing for a patient diagnosed with CSR. Imaging including fundus photos, OCT, and angiography can help diagnose CSR by identifying fluid detachments and leakages. While the cause is unknown, CSR is often self-limiting and treatments may include observation, anti-inflammatory drugs, or laser photocoagulation depending on severity and chronicity. This patient was initially observed but later prescribed NSAIDs due to the chronic nature of the detachment.
This document discusses diabetic retinopathy, which is a microvascular complication of longstanding diabetes mellitus that can cause retinal damage and vision loss. It defines the different classifications of diabetic retinopathy from non-proliferative to proliferative stages. Signs and symptoms, risk factors, pathogenesis, differential diagnoses, screening recommendations, and treatment options such as laser photocoagulation are described in detail. Case studies are suggested to apply the concepts.
Diabetic retinopathy is a leading cause of blindness that can be detected through automated analysis of fundus images. The document proposes using support vector machines to build a model that can robustly detect four key features of diabetic retinopathy - hard exudates, soft exudates, microaneurysms, and hemorrhages. The model is trained on a standardized set of fundus images and achieves over 95% accuracy on classification, providing an affordable solution to diagnose a disease affecting many people.
1. The document discusses recent treatments for diabetic macular edema (DME), including laser therapy, anti-VEGF drugs like ranibizumab and bevacizumab, and steroids.
2. It also covers screening guidelines, lifestyle management to control diabetes and risks factors, and potential future therapies like implants for sustained drug delivery.
3. Surgery options like vitrectomy are discussed when DME is severe, along with postsurgical follow up schedules.
This document provides an overview of diabetic retinopathy diagnosis through the analysis of retinal images. It discusses the aims of identifying patients with different stages of diabetic retinopathy. The stages of diabetic retinopathy and associated symptoms are defined. Pre-processing steps like color conversion, filtering and segmentation are described. A proposed methodology includes blood vessel and lesion detection through morphological operations, texture analysis, feature extraction and classification. Results of optic disc detection, blood vessel segmentation and texture analysis are shown. The conclusion discusses developing more accurate detection techniques and extracting smaller blood vessels to aid in diagnosis.
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
Optical Coherence Tomography in Multiple Sclerosisneurophq8
OCT is a non-invasive technology used in ophthalmology to assess retinal diseases and glaucoma. In recent years , OCT has been used to assess axonal loss and neurodegeneration in MS. This presentation will highlight the main uses of the OCT in MS and review of the literature.
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.
Eylea (aflibercept) is a treatment for wet age-related macular degeneration (AMD) and central retinal vein occlusion (CRVO). Phase 3 clinical trials found aflibercept to be effective in improving vision for both conditions compared to controls. For wet AMD, VIEW 1 and VIEW 2 trials found aflibercept non-inferior to ranibizumab. For CRVO, Copernicus found aflibercept superior to sham injections and Galileo found it superior to sham injections. Cost analysis found aflibercept increases health plan spending but is cost-effective compared to alternatives. The committee recommends keeping aflibercept non-preferred due to cost but reevaluating as more data is published
Optic neuritis is an inflammation of the optic nerve that can be divided into 3 types based on appearance: papillitis, neuroretinitis, and retrobulbar neuritis. Papillitis is most common in children and involves edema and hyperemia of the optic disc. Neuroretinitis also involves the retinal nerve fiber layer and macula. Retrobulbar neuritis most commonly affects adults and the optic disc may appear normal initially. Causes include demyelinating disorders like multiple sclerosis, infections, immune disorders, and metabolic conditions. Treatment involves steroids like intravenous methylprednisolone to hasten recovery of vision, with the goal of improving outcomes and delaying development of multiple sclerosis
1. Diabetic macular edema (DME) is a common retinal disorder where fluid collects in the macula due to leaky blood vessels, causing it to swell and blurring vision.
2. DME is diagnosed through a dilated eye exam, optical coherence tomography, and sometimes fluorescein angiography.
3. Treatment involves controlling diabetes and other risk factors, laser therapy to seal leaky blood vessels, and injections of anti-VEGF drugs which reduce new blood vessel growth and leakage.
Diabetic retinopathy is damage to the blood vessels of the retina due to diabetes. A study compared the standard treatment of pan-retinal photocoagulation (PRP) to initial treatment with intravitreal anti-VEGF injections (ranibizumab) to delay or prevent the need for PRP. At 2 years, vision was maintained or improved in the ranibizumab group while vision remained unchanged in the PRP group. The study suggests initial treatment of proliferative diabetic retinopathy with anti-VEGF injections like ranibizumab may be as effective as immediate PRP treatment and could help delay or reduce the need for destructive PRP therapy.
This document discusses diabetic retinopathy and its management. It begins by stating that diabetic retinopathy is the leading cause of vision impairment worldwide. It then describes the clinical presentation and stages of diabetic retinopathy from mild non-proliferative to proliferative, as well as diabetic macular edema. The management section discusses preventing and treating diabetic retinopathy through controlling blood sugar and blood pressure systematically, as well as local treatments like pan-retinal photocoagulation laser, anti-VEGF injections, and vitrectomy surgery. It emphasizes that timely screening and treatment according to guidelines can reduce the risk of severe vision loss to less than 5%.
This document provides guidelines for screening, monitoring, classifying severity, and treating diabetic macular edema (DME). It recommends annual screening of diabetic patients aged 15+ for retinopathy and treating any sight-threatening cases found. For DME treatment, it discusses traditional laser photocoagulation as well as newer options like intravitreal corticosteroids and anti-VEGF drugs. Intravitreal injections of anti-VEGF agents are considered first-line therapy for center-involving DME, with laser as an option for non-center cases or if thickening persists after anti-VEGF treatment. Strict control of modifiable risk factors like glycemia, blood pressure, and lipids can also help prevent
This document discusses macular edema in diabetic retinopathy and its treatment. It explains that macular edema is caused by chronic hyperactivity of the polyol pathway and increased vascular endothelial growth factor, which leads to increased vascular permeability. Laser photocoagulation and intravitreal anti-VEGF injections are effective treatments that work by reducing hypoxia and VEGF levels in the retina. Several studies found that treatments with drugs like ranibizumab and aflibercept were more effective at improving vision outcomes than laser alone in patients with diabetic macular edema.
Age related macular degeneration from Optometrist Point of ViewAnis Suzanna Mohamad
Age-related macular degeneration (ARMD) is a degenerative eye disease that affects the macula and can cause vision loss, with risk factors including aging, smoking, and family history. It has early and late stages, with the late stage including dry ARMD characterized by retinal pigment epithelium and photoreceptor atrophy, and wet ARMD involving abnormal blood vessel growth beneath the retina. Treatment options depend on the stage and type of ARMD, with anti-VEGF injections and photodynamic therapy used for wet ARMD to prevent further vision loss.
Retinal diseases can affect vision and cause blindness. Common retinal conditions include floaters, macular degeneration, diabetic eye disease, retinal detachment, and retinitis pigmentosa. Floaters are spots in vision caused by age-related changes in the vitreous jelly inside the eyes. Macular degeneration is deterioration of the macula and is diagnosed as dry or wet. Diabetics are susceptible to retinal damage from conditions like diabetic eye disease. Retinal detachment occurs when fluid builds behind the retina, causing separation. Retinitis pigmentosa describes genetic conditions that cause retinal degeneration over time.
Slides include
Basic anatomy of optic nerves
Background & epidemiology of optic neuritis
Classification of optic neuritis
Clinical features
Investigations
Diagnosis
Differential diagnosis
Managements
Prognosis
Central serous chorioretinopathy (CSR) is characterized by localized serous retinal detachment caused by leakage from the choroid through hyperpermeable areas of the retinal pigment epithelium (RPE). It typically affects males aged 25-55 and can be associated with stress, pregnancy, corticosteroid use, and obstructive sleep apnea. Patients may experience blurred vision, metamorphopsia, and micropsia. Diagnostic testing includes OCT, FA, and ICGA to detect choroidal vascular abnormalities and RPE leaks. While many cases resolve spontaneously, recurrent or chronic CSR can cause permanent vision loss and may be treated with corticosteroid cessation, laser, photodynamic
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.
Case Report and Clinical Findings of Central Serous RetinopathyDan Mulder
Central Serous Retinopathy (CSR) is a retinal disease caused by a serous detachment of the retina. The case report details examination findings and diagnostic testing for a patient diagnosed with CSR. Imaging including fundus photos, OCT, and angiography can help diagnose CSR by identifying fluid detachments and leakages. While the cause is unknown, CSR is often self-limiting and treatments may include observation, anti-inflammatory drugs, or laser photocoagulation depending on severity and chronicity. This patient was initially observed but later prescribed NSAIDs due to the chronic nature of the detachment.
This document discusses diabetic retinopathy, which is a microvascular complication of longstanding diabetes mellitus that can cause retinal damage and vision loss. It defines the different classifications of diabetic retinopathy from non-proliferative to proliferative stages. Signs and symptoms, risk factors, pathogenesis, differential diagnoses, screening recommendations, and treatment options such as laser photocoagulation are described in detail. Case studies are suggested to apply the concepts.
Diabetic retinopathy is a leading cause of blindness that can be detected through automated analysis of fundus images. The document proposes using support vector machines to build a model that can robustly detect four key features of diabetic retinopathy - hard exudates, soft exudates, microaneurysms, and hemorrhages. The model is trained on a standardized set of fundus images and achieves over 95% accuracy on classification, providing an affordable solution to diagnose a disease affecting many people.
Suneel Kumar Padala is seeking a position in the IT field. He has a Bachelor's degree in Electronics and Communication Engineering from SIR CR Reddy College of Engineering with a score of 7.2/10. His technical skills include Quality Centre, QTP, Core Java, Windows, Ubuntu, manual and automation testing, and VB scripting. He completed a project in MATLAB on detecting and grading severity levels of exudates using SVM classifier. His experience includes working as team lead on an HRMS project. He holds certifications in ISTQB foundation level and software testing. He has participated in academic and extracurricular activities and describes himself as a quick learner and team player.
Sagar Suraj Lachure is seeking a position that allows him to apply his knowledge and skills in computer science and keep up with new technologies. He has an M.Tech in computer science from Government College of Engineering, Amravati and a B.E. in IT from H.V.P.M COET, Amravati. His experience includes 6 months of teaching and working as an Assistant Professor at Yashawantrao Cavan College of Engineering since 2013. He has published several papers on topics like diabetic retinopathy detection and participated in various conferences. His skills include programming in C, C++, Java and MATLAB as well as using operating systems, databases and documentation software.
1. The document proposes a new approach for detecting microaneurysms in retinal images that combines multiple preprocessing methods and candidate extraction techniques before classification.
2. An ensemble-based system is used that applies various preprocessing like contrast enhancement and candidate extractors like circular Hough transformation before detecting microaneurysms, to improve flexibility and detection results.
3. Experimental results show the proposed combining approach outperforms individual candidate extraction methods for microaneurysm detection.
Manjushree Mashal is seeking a position in electronics and communication engineering where she can utilize her skills and help the organization achieve its goals. She has a Master's degree in digital communication and networking and relevant technical skills. During her internship at Saankhya Labs, she gained experience in wireless and digital communication areas like rural broadband products. She has published technical papers and completed projects on topics like diabetic retinopathy analysis and wireless sensor network performance comparison. She is self-motivated, hard-working and has participated in various academic and technical competitions.
As Diabetes Mellitus combined with other ailments will become a deadly combination, hence there
is an urgent need to break the link between diabetes and its related complications. For this purpose image
processing based analysis can potentially be helpful for earlier detection, education and treatment. Medical
image analysis of Diabetic patients with its related complications such as DR, CVD & Diabetic
Myonecrosis (i.e. on Retinal Images, Coronary angiographs, Electron micrographs, MRI etc) is to be the
aprioristic because of its more prevalence. Thus the main work of this paper is on literature review about
Diabetes and Imaging such as the Prevalence, Classification, Causes and Medical Imaging & Survey of
Image processing methods applied on Diabetic Related Causes.
Keywords — Image, segmentation, retinopathy, Myonecrosis,
Mark Cahill, Global Vision National Diabetic Retinopathy Screening ProgrammeInvestnet
This document summarizes Ireland's National Diabetic Retinal Screening Programme. It discusses that diabetic retinopathy is a leading cause of blindness and annual screening can prevent blindness in 96% of cases. The programme screens over 145,000 people with diabetes annually using digital photographs of the retina that are graded by qualified reviewers. Those found to have signs of diabetic retinopathy or macular edema are referred to hospital treatment centers for further evaluation and care. The programme aims to screen 30% of the eligible population in its first year and 70% in the second year.
Teamed with 2 students to research and implement the automation of diagnosis of Diabetic Retinopathy and co-ordinated with an Ophthalmologist to verify our implementation.
Responsibilities included MATLAB coding, algorithm testing, and product documentation.
• Automation in MATLAB involving retinal image analysis to help
Ophthalmologist increase the productivity and efficiency in a clinical
environment.
• Used Image Processing concepts such as Hough Transform, Bottom Hat
Transform, Edge Detection Technique and Morphological Operators.
Provided our algorithm and documentation to our research faculty advisor to enable him to continue this research to the next phase.
This document provides an overview of gonioscopy, including:
1. It describes the history and development of gonioscopy, from early pioneers like Trantas who coined the term, to modern innovations like the Goldmann lens.
2. The purpose and principles of gonioscopy are explained, such as allowing visualization of the anterior chamber angle to assess for angle closure risk factors.
3. Different techniques for gonioscopy are covered, including direct methods using a Koeppe lens and indirect methods using multi-mirror lenses like the Goldmann lens viewed through a slit lamp.
4. Common angle structures are defined, and grading systems for assessing angle width and other characteristics are outlined.
Gonioscopy: gonioscopic lenses, principle and clinical aspectsDr Samarth Mishra
This document discusses gonioscopy, which is used to examine the anterior chamber angle. It begins by explaining that the angle cannot be viewed directly due to total internal reflection at the cornea. Gonioscopic lenses eliminate this effect by matching the cornea's refractive index. There are two main types of lenses - indirect lenses use mirrors and direct lenses refract light. The document then describes various gonioscopic lenses and techniques like indentation gonioscopy. It outlines the clinical uses of gonioscopy and provides examples of gonioscopic findings. In summary, the document provides an in-depth overview of gonioscopy equipment, techniques, and applications.
The document summarizes different techniques used to examine the fundus of the eye, including:
1. Direct ophthalmoscopy provides an upright 15x view of the posterior pole but no stereopsis. Indirect ophthalmoscopy uses lenses from 14-30D for an inverted 40-50 degree view with stereopsis.
2. Slit lamp biomicroscopy provides a 30-40 degree inverted view at higher magnifications up to 16x using a +78D lens.
3. A -55D lens provides a virtual upright view for examining the optic nerve and retina through dilated pupils. A contact lens combines high magnification and stereopsis for detailed macular exams.
4. Wide
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
Automatic detection Non-proliferative Diabetic Retinopathy using image proces...IJERA Editor
This document summarizes research on automatically detecting non-proliferative diabetic retinopathy using image processing techniques. It begins with background on diabetes and diabetic retinopathy. Diabetic retinopathy is classified into non-proliferative and proliferative stages. Non-proliferative diabetic retinopathy involves microaneurysms, hemorrhages, hard exudates, and soft exudates. The research aims to automatically detect the stages of diabetic retinopathy in retinal images in order to help prevent vision loss from the disease. It describes collecting retinal images, preprocessing them, and developing techniques to analyze the images and detect signs of non-proliferative diabetic retinopathy.
This document discusses automatic detection of microaneurysms in digital fundus images using local region entropy (LRE). It proposes a computer-based system for automatically detecting diabetic retinopathy in fundus images using an SVM classifier. The system achieves 95.38% accuracy, 94% sensitivity, and 94.7% specificity when tested on the DIARETDB1 dataset containing 89 images. Diabetic retinopathy occurs when high blood sugar damages the blood vessels in the retina and can lead to vision loss or blindness if left untreated. The proposed system would help reduce the workload of ophthalmologists by automatically analyzing fundus images to detect signs of the disease.
Glaucoma Screening Using Novel Evaluated CNN Architecture: An Automated Appro...IRJET Journal
The document proposes a convolutional neural network (CNN) architecture to detect glaucoma from fundus images. It involves preprocessing images using techniques like resizing, grayscale conversion, and CLAHE. The images are then segmented using Canny edge detection. An 8-layer CNN is trained on labeled images to classify images as normal or glaucoma based on extracted features. The proposed method achieves high classification accuracy, demonstrating its reliability for early glaucoma detection through automated analysis of fundus images.
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.
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.
Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopath...ITIIIndustries
1) The document discusses the development of a software to annotate retina fundus images for teaching and learning about diabetic retinopathy.
2) The authors collected 625 fundus images from Bangladesh and designed annotation software to store patient information, infection levels, and locations in the images. They introduced a K-nearest neighbor technique to accurately select regions of interest since it can be difficult for experts.
3) Once experts annotate the images using this software, it can be used by students for learning about diabetic retinopathy stages and symptoms. The dataset and software aim to address the lack of proper diabetic retinopathy datasets and aid medical education.
Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopath...Carrie Cox
1) The document discusses the development of a software to annotate retina fundus images for teaching and learning about diabetic retinopathy.
2) The authors collected 625 fundus images from Bangladesh and designed annotation software to store patient information, infection levels, and locations in the images.
3) They introduced a K-nearest neighbor technique to accurately select regions of interest, as it can be difficult for experts to select all appropriate pixels of infected regions. The annotated images and software can then be used by medical students for learning about diabetic retinopathy.
IRJET- Study on Segmentation and Classification Techniques for Automatic ...IRJET Journal
This document discusses techniques for the automatic detection of microaneurysms, which are small red spots that are early signs of diabetic retinopathy. It proposes using image segmentation and classification methods on retinal images to identify microaneurysms. Specifically, it compares the performance of adaptive k-means clustering and fuzzy c-means segmentation, as well as support vector machine (SVM) and probabilistic neural network (PNN) classification. The document provides background on diabetic retinopathy and microaneurysms. It also reviews related work on segmentation and classification methods for detecting lesions in retinal images.
AUTOMATIC DETECTION OF SEVERITY GRADING IN DIABETIC RETINOPATHY USING CONVOLU...IRJET Journal
This document discusses automatic detection of severity grading in diabetic retinopathy using convolutional neural networks. Diabetic retinopathy is a leading cause of preventable blindness that can be detected early through retinal screening. Previously this required trained clinicians to manually examine retinal images, but convolutional neural networks have shown superior performance for automated image classification. The document proposes a CNN-based approach for automated screening of retinal images to detect different severity levels of diabetic retinopathy and reduce the workload on ophthalmologists.
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
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.
IRJET - Classification of Retinopathy using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to classify different types of retinopathy from retinal images. It begins with an abstract that outlines using feature extraction and the K-Nearest Neighbor algorithm to predict and classify disease in retinal images. The introduction then defines different types of retinopathy including diabetic retinopathy, hypertensive retinopathy, and retinopathy of prematurity. The literature review discusses research on retinal vascular development in premature infants, a classification system for retinopathy of prematurity, and automated detection of diabetic retinopathy from digital fundus images.
Diabetic Retinopathy Detection using Neural Networkingijtsrd
1. The document presents a neural network approach for detecting diabetic retinopathy from retinal images. A CNN is trained on a dataset of 3,662 images to classify the stages of diabetic retinopathy from no DR to proliferative DR.
2. The neural network is fine-tuned using transfer learning with a ResNet50 model. It achieves 78% accuracy on a validation set of 733 images.
3. Diabetic retinopathy cannot be cured, so the goal is to detect it early to prevent vision loss. This system aims to classify DR stages from retinal images to identify the disease early and help prevent further vision loss.
Disease Of Retina And Its Treatment.pdfSkipper Eye-Q
The retina, a delicate and vital layer of tissue at the back of the eye, plays a critical role in our
ability to see. It captures light and converts it into nerve signals sent to the brain for visual processing.
The document provides information on visual impairment and eye diseases that commonly affect the elderly. It discusses causes of visual loss like cataract and age-related macular degeneration. It describes treatments for these conditions like cataract surgery and laser photocoagulation. It also covers other diseases like diabetic retinopathy and glaucoma that can cause vision loss in older adults.
This document discusses a drug delivery device to treat diabetic retinopathy, a complication of diabetes that affects the retina. Diabetic retinopathy is classified as either non-proliferative or proliferative and can progress through different stages as the disease worsens over time if not treated. The document outlines the structure and function of the eye, types of diabetes, pathophysiology of diabetic eye disease, and current treatments for diabetic retinopathy which aim to prevent vision loss through early detection and management of risk factors like blood glucose control. A new drug delivery device is proposed to provide an improved treatment option for this condition.
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.
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%
Similar to Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K Means and Neural Network (20)
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
ISPM 15 Heat Treated Wood Stamps and why your shipping must have one
Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K Means and Neural Network
1. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE)
e-ISSN: 2278-1676,p-ISSN: 2320-3331, Volume 6, Issue 1 (May. - Jun. 2013), PP 22-27
www.iosrjournals.org
www.iosrjournals.org 22 | Page
Detection of Exudates Caused By Diabetic Retinopathy in Fundus
Retinal Image Using Fuzzy K Means and Neural Network
T.Vandarkuzhali,1
,C.S.Ravichandran2
,D.Preethi3,
Assistant professor1
,Dean of EEE2
, PG Scholar3
Hindusthan college of Engineering and Technology(1)
,
Sri Ramakrishna Engineering college(2)
,
Hindusthan college of Engineering and Technology(3)
Abstract: Image processing is unavoidable in modern ophthalmology , as it heavily dependent on visually
oriented signs. The various diseases which will affect the eye can be found with the help of digital fundus image.
In manual analysis, due to unavailability of the trained ophthalmologist, the diagnosis of retinal diseases
becomes unclear. Thus, automated analysis of fundus image is very much essential and will help to facilitate
clinical diagnosis. An automated system for the detection of various abnormalities due to diabetic retinopathy
(DR) in retinal image is presented here. Fuzzy logic and neural network is used to identify the abnormalities in
the fovea. These are evaluated for both normal as well as affected retinal images. A high performance language
for technical computing MATLAB, is used here to implement the concept.
Keywords- Diabetic Retinopathy, Fovea, Fundus retlnal image, Fuzzy K means, Feed Forward Neural Network.
I. Introduction
A .Human Eye
The human eye has been called the most complex organ in our body. Human eye is divided into three
parts Anatomy : structure of an eye .Physiology: function of these structure Pathology: disease and disorder of
these structure
Fig. 1 Human Eye
The cornea is the clear front window of the eye that transmits and focuses light into eye. The iris is the coloured
part of the eye that helps regulate the amount of light that enters the eye. The pupil is the dark aperture in the iris
that determines how much light is let into the eye. The lens is the transparent structure inside the eye that
focuses light rays onto the retina. The optic nerve is the nerve that connect the eye to the brain and carries the
impulse formed by the retina to the visual cortex of the brain. The vitreous humour is a clear , jelly like
substance that fills the middle of the eye .Macula is the centre most part of the retina and fovea is the centre of
the macula
B Retina
Retina converted light raise into electrical impulses and send toward brain through optic nerve
Contain rods and cones. Rods are responsible for light and dark adaptation ,Cones are responsible for color
vision[5] .Four kinds of light-sensitive receptors are found in the retina: rods and three kinds of cones, each
"tuned" to respond best to light from a portion of the spectrum of visible light
2. Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K
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Fig. 2 Rods and Cones
C Fundus Image
The colour fundus images are used to keep track of the eye diseases by ophthalmologist. Developing
automatic fundus image analyzing and diagnosis. Extraction of the normal and abnormal features in colour
fundus images is fundamental and useful to automatic understanding of fundus images. The normal features of
fundus images include,opticdisk,fovea,blood,vessels,etc[2],[5],[6].and abnormal features include exudates,
haemorrhages, microneurysms.
D Diabetes
Diabetes is due to high sugar level in blood. There are two types of Insulin-dependent caused when
beta cell in the pancreas get damaged develops it will be occur most frequently between 10 and 20 years of age.
Non-insulin-dependent diabetes (NIDD): Type 2.It will be caused when cell of body resistance against insulin
occur most frequently between the ages of 50 and 70 years. Diabetic retinopathy is a serious complication of
diabetes.
E Diabetic Retinopathy
Diabetic retinopathy, is retinopathy (damage to the retina) caused by complications of diabetes, which
can eventually lead to blindness. It is an ocular manifestation of systemic disease which affects up to 80% of all
patients who have had diabetes for 10 years or more. Despite these intimidating statistics, research indicates that
at least 90% of these new cases could be reduced if there was proper and vigilant treatment and monitoring of
the eyes. The longer a person has diabetes, the higher his or her chances of developing diabetic retinopathy.
Fig.3 Retina with Diabetic Retinopathy
T here are three main type of retinopathy
Background retinopathy: Small red dots will appear on retina due to tiny swellings in the blood vessel walls.
The number of microaneurysms, the little red dots the doctor sees, indicate the likelihood of more severe
problems in the years to come. As the damage is mild at this stage, your sight will be nearly perfect. However,
the condition does progress. If you have been diabetic 30 years, even with the best control, these may develop.
But most people who have background retinopathy have not been diabetic that long, and need better control as
per these targets.
BDR consists of:
Microaneurisms: these are usually the earliest visible change in retinopathy seen on exam with an
ophthalmoscope as scattered red spots in the retina where tiny, weakened blood vessels have ballooned out.
Hemorrhages: bleeding occurs from damaged blood vessels into the retinal layers. This will not affect
vision unless the bleeding occurs in or near the Macula.
Hard Exudates: caused by proteins and lipids from the blood leaking into the retina through damaged
blood vessels. They appear on the ophthalmoscope as hard white or yellow areas, sometimes in a ringlike
structure around leaking capillaries. Again vision is not affected unless the macula is involved.
3. Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K
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Fig.4 Background Retinopathy
Pre-proliferative retinopathy: Retina swells and leaks blood reading small print may become particularly
difficult. In this condition the retina has been damaged by the higher than normal sugar levels over several
years. The condition is called 'pre-proliferative' as it usually progresses to develop proliferative retinopathy,
when 'new vessels' develop. It is now generally termed 'non-proliferative'.
In severe forms of pre-proliferative retinopathy there are lot of haemorrhages, as the retina is very ischaemic.
This needs laser treatment to prevent new vessel growth. Proliferative retinopathy in one eye is especially likely
if the other eye has already developed new vessels.
Fig.5 Pre-Proliferative retinopathy
Proliferative retinopathy: It is third stage of retinopathy usually causing a sudden loss of vision In this
condition very small blood vessels grow from the surface of the retina.
The retina is the film at the back of your eye , and the tiny blood vessels are capillaries. These growing blood
vessels are very delicate and bleed easily. Without laser treatment, the bleeding causes scar tissue that starts to
shrink and pull the retina off, and the eye becomes blind. Lasertreatment prevents blindness, but often some
vision is lost.
If you have had diabetes for years your retinae may develop this condition. As the retina is damaged by diabetes,
the diseased retina releases special growth chemicals. These chemicals make tiny blood vessels grow: these are
called 'new blood vessels'.
Another serious complication of proliferative diabetic retinopathy is neovascular glaucoma. This occurs when
abnormal blood vessels grow on the iris and over the drainage system of the eye (the trabecular meshwork). This
can lead to extremely elevated eye pressure, pain, redness, and severe vision loss.
Fig.6.Proliferative Retinopathy
F Exudates
Exudates are accumulations of lipid and protein that oozes out of blood vessels due to inflammation and are
deposited in nearby tissues[3]. They are typically bright , reflective, white or cream colored lesions seen on the
retina . if this occurs on the macula, the vision may be lost
Fig.7. Retina with Exudates
4. Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K
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II. Problem Formulation
For non-invasive treatment, great effort from opthamologist is required. Diagnostics of retinal diseases
include complex examination by retinologist and fundoscopy. In the existing method Fuzzy C Means is used
which is not efficient in clustering. so, the Fuzzy K Means is used which is efficient in clustering .If the
clustering is done efficiently, the diagnosis of the exudates will be accurate and the treatment can be done
accordingly.
III. Implementation
In this paper, the exudates due to diabetic retinopathy is identified using the fuzzy k means
A Pre-processing done by Histogram Equalization
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram.
This method usually increases the global contrast of many images, especially when the usable data of the image
is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the
histogram. This allows for areas of lower local contrast to gain a higher contrast. Histogram equalization
accomplishes this by effectively spreading out the most frequent intensity values. The method is useful in
images with backgrounds and foregrounds that are both bright or both dark. In particular, the method can lead to
better views of bone structure in x-ray images, and to better detail in photographs that are over or under-
exposed. Histogram equalization often produces unrealistic effects in photographs; however it is very useful for
scientific images like thermal, satellite or x-ray images, often the same class of images that user would
apply false-color to. The probability of an occurrence of a pixel of level i in the image is
Histrogram equalization of color image
This describes histogram equalization on a grayscale image. However it can also be used on color images by
applying the same method separately to the Red, Green and Blue components of the RGB color values of the
image. However, applying the same method on the Red, Green, and Blue components of an RGB image may
yield dramatic changes in the image's color balance since the relative distributions of the color channels change
as a result of applying the algorithm.
Fig.8 Original image
Fig.9 Equalized image
B Fuzzy K Means
Fuzzy K-Means (also called Fuzzy C-Means) is an extension of K-Means, the popular simple
clustering technique. While K-Means discovers hard clusters (a point belong to only one cluster), Fuzzy K-
Means is a more statistically formalized method and discovers soft clusters where a particular point can belong
to more than one cluster with certain probability.
The algorithm is similar to k-means. Initialize k clusters until converged Compute the probability of a
point belong to a cluster for every <point, cluster> pair Recomputed the cluster centres using above probability
membership values of points to clusters. In data mining, k-means clustering is a method of cluster
5. Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K
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analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster
with the nearest mean. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard), however there are efficient heuristic algorithms that are
commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-
maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed
by both algorithms. Additionally, they both use cluster centers to model the data, however k-means clustering
tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows
clusters to have different shapes.
The FKM algorithm is based on minimizing the following distortion:
Jq(U,V) = ΣiΣk(uik)q
d2
(Xj – Vi); K N ----------------- (1)
Compute the degree of membership of all feature vectors in all clusters:
uij = [1/d2
(Xj – Vi)]1/(q-1)
/ Σk [1/ d2
(Xj – Vi)]1/(q-1) ----------
(2)
Under the constraint: Σiuik = 1 Compute new cluster prototypes Vi
Vi=Σj[(uij)q
Xj]/Σj(uij)q
--------------------------------- (3)
C Back Propagation Neural Network
Back propagation is a common method of training artificial neural networks so as to minimize the objective
function. and described it as a multi-stage dynamic system optimization method in . when applied in the context
of neural networks and through the work of that it gained recognition, and it led to a “renaissance” in the field
of artificial neural network research. It is a supervised learning method, and is a generalization of the delta rule.
It requires a dataset of the desired output for many inputs, making up the training set The back propagation
learning algorithm can be divided into two phases: propagation and weight update.
Phase 1: Propagation
Each propagation involves the following steps:
Forward propagation of a training pattern's input through the neural network in order to generate the
propagation's output activations. Backward propagation of the propagation's output activations through the
neural network using the training pattern's target in order to generate the deltas of all output and hidden neurons.
Phase 2: Weight update
For each weight-synapse follow the following steps:
Multiply its output delta and input activation to get the gradient of the weight.
Bring the weight in the opposite direction of the gradient by subtracting a ratio of it from the weight.
This ratio influences the speed and quality of learning; it is called the learning rate.
The sign of the gradient of a weight indicates where the error is increasing; this is why the weight must be
updated in the opposite direction.
Repeat phase 1 and 2 until the performance of the network is satisfactory.
IV. Results
By using this software, automatic detection of exudates due to Diabetic retinopathy is achieved within
short period of time. The accuracy and efficiency is increased when compared to Fuzzy C Means. Normal
retinal images and affected images are used for the experiment. The following images were obtained when
stimulated using MATLAB for normal and affected images.
Input Image Clustered Image
(a)
6. Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image Using Fuzzy K
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Input Image Clustered Image
(b)
Fig.10 Input and Output images a)Norma Image B)Abnormal image
V. Conclusion And Future Scope
In the paper, an efficient method to identify the exudates due to diabetic retinopathy in fundus retinal
images is described.FuzzyK means and the NeuralNetwork were used.This scheme is simple and efficient in
extracting wheather the image is in normal or in abnormal stage.The extracted abnormal images may help in
future diagnosis of related diseases.This work is performed for both normal and abnormal image.The
extraction time for detecting diseases using Fuzzy K Means is very less when compared ti that of other
technique.The continuation of this work is to be implemented in morphological method used to identify the
hemorrages.
Acknowledgement
The authors would like to thank Dr. D.Poovannnan Assisant Doctor,Kovai Diabetes Specality Centre
and Hospital for their valuable suggestions and help in obtaining the images used in the research.
References
[1] Adam Hoover* and Michael Goldbaum”Locating the Optic Nerve in a Retinal Image using the Fuzzy Convergence of the Blood
Vessels”IEEE transcations on medical imaging, vol.22,no.8.august 2003
[2] Anderson Rocha ∗, Member, IEEE , Tiago Carvalho, Herbert F. Jelinek , Member, IEEE , Siome Goldenstein , Senior Member,
IEEE, and Jacques Wainer“Points of Interest and Visual Dictionaries for Automatic Retinal Lesion Detection” 2244 IEEE
transactions on biomedical engineering, vol. 59, no. 8, august 2012.
[3] Adam Hoover*, Valentina Kouznetsova, and Michael Goldbaum“Locating Blood Vessels in Retinal Images by Piecewise
Threshold Probing of a Matched Filter Response” IEEE Transactions on medical imaging, vol. 19, no. 3, march 2000 203
[4] F. Zana* and J. C. Klein“ A Multimodal Registration Algorithm of Eye Fundus Images.Using Vessels Detection and Hough
Transform” .IEEE Transactions on medical imaging, vol. 18, no. 5, may 1999 419.
[5] Jeetinder Singh, Joshi G.D. Sivaswamy “Appearance based Object Detection in Colour Retinal Images” Proc. Of IEEE
International conference on Images processing (ICIP), Oct 12-15,2008,San Diego,USA.
[6] Michal D. Abramoff, Senior Member ,IEEE, Monak k. Garvin , Member IEEE and Milan Sonak.Fellow, IEEE.“Retinal Imaging
and Image Analysis” IEEE Reviews in Biomedical engineering , vol3,2010.
[7] Opas Chutatape , Senior Member, IEEE Proceedings of the 28th
IEEE EMBS annual International conference New york city,
USA ,Aug 30-sept3,2006. ]“Fundus Foveal Localisation Based on Vessel Model”
[8] Soumitra Samanta,Sanjoy kumar sahaand bhabatose Chanda,1 ECSU, Indian.StatisticalInstitute,Kolkata,2 CSE department,jadavpur
university, kolkata, india”A Simple and Fast Algorithm to Detect the Fovea Region in Fundus Retinal Images.