This document describes a method for identifying diabetic retinopathy using retinal images. The aim is to efficiently identify diabetic retinopathy by detecting exudates, a key feature. Exudates are identified using k-means clustering and a naive Bayes classifier. The method involves pre-processing images, segmenting images using k-means clustering to label pixels, extracting features based on color and texture, and classifying images as exudates or non-exudates using naive Bayes. The approach detects exudates with 98% success rate and could potentially be expanded to detect other features of diabetic retinopathy like microaneurysms.
This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.
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.
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.
The document presents a method for the automatic detection of blood vessels in retinal images. The method uses preprocessing, Hessian multiscale enhancement filtering, and adaptive thresholding. It is tested on three retinal image databases and achieves higher sensitivity, specificity, and accuracy than some state-of-the-art methods. Automatic detection of blood vessels is important for diagnosing and treating retinal diseases like diabetic retinopathy.
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 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.
Small overview of the startups involved in healthcare artificial intelligence, the OCT market, investments, patent and IP issues and FDA regulation.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/retinalAI_landscape.pdf
This document describes a method for identifying diabetic retinopathy using retinal images. The aim is to efficiently identify diabetic retinopathy by detecting exudates, a key feature. Exudates are identified using k-means clustering and a naive Bayes classifier. The method involves pre-processing images, segmenting images using k-means clustering to label pixels, extracting features based on color and texture, and classifying images as exudates or non-exudates using naive Bayes. The approach detects exudates with 98% success rate and could potentially be expanded to detect other features of diabetic retinopathy like microaneurysms.
This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.
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.
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.
The document presents a method for the automatic detection of blood vessels in retinal images. The method uses preprocessing, Hessian multiscale enhancement filtering, and adaptive thresholding. It is tested on three retinal image databases and achieves higher sensitivity, specificity, and accuracy than some state-of-the-art methods. Automatic detection of blood vessels is important for diagnosing and treating retinal diseases like diabetic retinopathy.
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 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.
Small overview of the startups involved in healthcare artificial intelligence, the OCT market, investments, patent and IP issues and FDA regulation.
Alternative download link: https://dl.dropboxusercontent.com/u/6757026/slideShare/retinalAI_landscape.pdf
How AI Enhances & Accelerates Diabetic Retinopathy DetectionCognizant
To enable earlier and quicker diagnosis of diabetic retinopathy (DR), Cognizant has built a system based on AI and deep learning - a convolutional neural network - that analyzes many thousands of fundus images and delivers accurate assessments of eye-disease damage.
This document describes a deep learning approach for detecting diabetic retinopathy using OCT images. It discusses the proposed system which will use OCT images and apply classification algorithms to identify the level of infection. The model will be trained on datasets of infected images to accurately detect regions of infection and the condition level. Image processing techniques like median filtering and edge detection will be used along with statistical data extraction and supervised training to identify clusters and classify images. Results will be compared to evaluate the machine learning models. The system aims to automate diabetic retinopathy detection to improve efficiency over conventional methods.
1. The document discusses the relationship between visual acuity and retinal thickness in diabetic macular edema (DME) as measured by optical coherence tomography (OCT). While visual acuity only modestly correlates with foveal thickness, DME affects the entire macula.
2. A study found that changes in OCT-measured central retinal thickness did not strongly correlate with changes in visual acuity in DME patients. Inflammation plays an important early role in the development of DME before fluid accumulation occurs.
3. Which anti-VEGF drug is best for DME treatment - ranibizumab, bevacizumab, or aflibercept - remains unclear based on real-world evidence,
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
Artificial intelligence has made significant advancements in ophthalmology by analyzing medical images and data. AI algorithms can detect eye diseases like diabetic retinopathy and macular degeneration from retinal images, predict disease risk and progression, and provide treatment recommendations to augment doctors. While AI shows promise in improving diagnosis and access to eye care, limitations include potential data and generalization biases that require addressing through responsible development and validation of these new technologies.
This document discusses diabetic retinopathy, including its classification, risk factors, and evidence from studies on the importance of glycemic control. It covers different classification systems based on features, fluorescein angiography, and OCT. National screening programs are outlined that use digital retinal photography to detect retinopathy and sight-threatening diabetic retinopathy. Guidelines recommend annual eye exams for those with diabetes to monitor for retinopathy and referrals for proliferative retinopathy or other complications.
Acute Rise in IOP (Dr. Rasha, senior resident of ophthalmology)Hind Safwat
There are several potential causes of acute increases in intraocular pressure (IOP), including glaucomatocyclitic crisis (Posner-Schlossman syndrome), inflammatory open-angle glaucoma, retrobulbar hemorrhage or inflammation, traumatic glaucoma, pigmentary glaucoma, neovascular glaucoma, plateau iris syndrome, and malignant glaucoma. IOP increases above 40mmHg can rapidly damage the optic nerve and cause permanent vision loss within hours. Treatment depends on the underlying cause but generally involves topical medications to lower IOP such as beta-blockers, alpha-2 agonists, and carbonic anhydrase inhibitors as well as systemic therapies like oral acetazol
"The image shows no signs of diabetic macular edema."
If exudates are found between 1DD to 2DD from the fovea, it is classified as less
significant stage. The
The document discusses diabetic retinopathy, including its definition, signs and symptoms, causes, risk factors, stages, treatments including laser photocoagulation and intravitreal injections, and importance of glycemic and blood pressure control. It emphasizes the need for regular eye exams in people with diabetes to screen for and treat diabetic eye diseases early.
Detection of eye disorders through retinal image analysisRahul Dey
This document describes methods for detecting eye disorders through retinal image analysis. It discusses segmenting blood vessels and the optic disk using algorithms. It also covers applying fuzzy logic image processing to enhance edge detection of blood vessels. The proposed approach uses a Mamdani fuzzy inference system on a moving window to classify edges based on gradient inputs and Gaussian membership functions. Simulation results show the fuzzy method enhances edge detection compared to common methods like Canny, Sobel, and Prewitt.
The Advanced Glaucoma Intervention Study was a multicenter randomized clinical trial that compared two sequences of surgical treatment for advanced glaucoma. It involved 789 eyes of 591 patients followed over 13 years. The study found that lower IOP was associated with less visual field progression. It also found differences in outcomes between black and white patients and between the two treatment sequences of initial trabeculectomy versus initial augmented trabeculotomy. The study provided important information about risk factors for surgical failure and progression of visual loss in advanced glaucoma.
This document discusses a study on knowledge and awareness of diabetic eye conditions among type 2 diabetic patients in Buraidah, Saudi Arabia. The study aimed to assess patients' baseline knowledge using a questionnaire, provide an educational booklet, and re-assess knowledge. Results showed that after the intervention, patients' knowledge increased significantly from 51% correct pre-intervention to 86% post-intervention. The study highlights the importance of education in improving diabetic patients' understanding of eye complications to facilitate early detection and treatment.
This document summarizes a seminar on assessing the visual field using automated perimetry. It defines key terminology used in visual field testing like threshold, apostilbs, decibels, and indices. It describes the components and functioning of the Humphrey Field Analyzer automated perimeter. It provides criteria for diagnosing glaucoma based on visual field tests and categorizes the severity of visual field defects as early, moderate, or severe. It also outlines how to recognize progressive damage by comparing tests over time.
The Ocular Hypertension Treatment Study (OHTS) was a landmark randomized controlled trial that showed treating patients with ocular hypertension reduced the risk of developing primary open-angle glaucoma by more than 50% compared to observation alone. Increased risk factors for developing glaucoma included older age, larger cup-to-disc ratios, higher baseline intraocular pressure, and thinner central corneal thickness. The Early Manifest Glaucoma Trial found that treating newly diagnosed glaucoma patients lowered their intraocular pressure by 25% on average and reduced the risk of visual field progression by about 20% compared to no treatment. The Collaborative Initial Glaucoma Treatment Study found that both medical and surgical treatment were effective for initially lowering intra
Diabetic retinopathy is caused by chronic hyperglycemia leading to progressive dysfunction of the retinal vasculature. This causes vascular leakage, focal ischemia, retinal hypoxia and neovascularization as well as thickening of the basement membrane and loss of pericytes impairing oxygen and nutrient flow. The stages include non-proliferative diabetic retinopathy characterized by microaneurysms and hemorrhages, and proliferative diabetic retinopathy characterized by new vessel growth. Macular edema can also occur from fluid leakage causing vision loss.
OCT-Angiography is a non-invasive imaging method that uses light to visualize the retinal and choroidal vasculature in 3D without dye injection. It works by detecting the movement of red blood cells on sequential OCT scans to identify blood vessels. The document describes the technical aspects and clinical applications of several commercial OCT-Angiography systems.
Landuse Classification from Satellite Imagery using Deep LearningDataWorks Summit
With the abundance of remote sensing satellite imagery, the possibilities are endless as to the kind of insights that can be derived from them. One such use is to determine land use for agriculture and non-agricultural purposes.
In this talk, we’ll be looking at leveraging Sentinel-2 satellite imagery data along with OpenStreetMap labels to be able to classify land use as agricultural or non-agricultural.
Sentinel-2 data has a 10-meter resolution in RGB bands and is well-suited for land use classification. Using these two datasets, many different machine learning tasks can be performed like image segmentation into two classes (farm land and non-farm land) or more challenging task of identification of crop type being cultivated on fields.
For this talk, we’ll be looking at leveraging convolutional neural networks (CNNs) built with Apache MXNet to train deep learning models for land use classification. We’ll be covering the different deep learning architectures considered for this particular use case along with the appropriate metrics.
We’ll be leveraging streaming pipelines built on Apache Flink and Apache NiFi for model training and inference. Developers will come away with a better understanding of how to analyze satellite imagery and the different deep learning architectures along with their pros/cons when analyzing satellite imagery for land use. SUNEEL MARTHI and CHRIS OLIVIER, Software Development Engineer Amazon Web Services
This document discusses diabetic retinopathy, including:
- The two main types of diabetes and how they relate to retinopathy risk and onset age.
- Diabetic retinopathy as a leading cause of blindness and its impact.
- Key risk factors like diabetes duration, glycemic control, and other systemic factors.
- The characteristic lesions and stages of non-proliferative and proliferative retinopathy.
- Treatment approaches including laser photocoagulation, anti-VEGF injections, steroids, and surgery.
- Screening guidelines based on diabetes type and risk level.
Smart Presentation Control by Hand Gestures Using Computer Vision and Google’...IRJET Journal
This document describes a smart presentation control system using hand gesture recognition with computer vision and Google's MediaPipe framework. The system uses a webcam to capture videos and photos of hand gestures as input. MediaPipe is used to detect hand landmarks and gestures in real-time. Various hand gestures like changing slides, drawing on slides, and erasing can be used to control the presentation without needing a keyboard or mouse. The system aims to provide a natural and intuitive human-computer interaction experience for presentation control through hand gesture recognition.
Automatic Detection of Diabetic Maculopathy from Fundus Images Using Image An...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
Automatic Blood Vessels Segmentation of Retinal ImagesHarish Rajula
The document discusses automatic segmentation of blood vessels in retinal images. It presents a proposed system that uses morphological operations and an SVM classifier for blood vessel segmentation. The system first enhances retinal images using histogram equalization. It then processes the green channel using morphological operations like dilation and erosion. Features are extracted from the processed image and used to train an SVM classifier to detect and segment blood vessels. The proposed method achieved an average sensitivity of 78%, specificity of 97.99%, and accuracy of 99.6% on retinal images.
How AI Enhances & Accelerates Diabetic Retinopathy DetectionCognizant
To enable earlier and quicker diagnosis of diabetic retinopathy (DR), Cognizant has built a system based on AI and deep learning - a convolutional neural network - that analyzes many thousands of fundus images and delivers accurate assessments of eye-disease damage.
This document describes a deep learning approach for detecting diabetic retinopathy using OCT images. It discusses the proposed system which will use OCT images and apply classification algorithms to identify the level of infection. The model will be trained on datasets of infected images to accurately detect regions of infection and the condition level. Image processing techniques like median filtering and edge detection will be used along with statistical data extraction and supervised training to identify clusters and classify images. Results will be compared to evaluate the machine learning models. The system aims to automate diabetic retinopathy detection to improve efficiency over conventional methods.
1. The document discusses the relationship between visual acuity and retinal thickness in diabetic macular edema (DME) as measured by optical coherence tomography (OCT). While visual acuity only modestly correlates with foveal thickness, DME affects the entire macula.
2. A study found that changes in OCT-measured central retinal thickness did not strongly correlate with changes in visual acuity in DME patients. Inflammation plays an important early role in the development of DME before fluid accumulation occurs.
3. Which anti-VEGF drug is best for DME treatment - ranibizumab, bevacizumab, or aflibercept - remains unclear based on real-world evidence,
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
Artificial intelligence has made significant advancements in ophthalmology by analyzing medical images and data. AI algorithms can detect eye diseases like diabetic retinopathy and macular degeneration from retinal images, predict disease risk and progression, and provide treatment recommendations to augment doctors. While AI shows promise in improving diagnosis and access to eye care, limitations include potential data and generalization biases that require addressing through responsible development and validation of these new technologies.
This document discusses diabetic retinopathy, including its classification, risk factors, and evidence from studies on the importance of glycemic control. It covers different classification systems based on features, fluorescein angiography, and OCT. National screening programs are outlined that use digital retinal photography to detect retinopathy and sight-threatening diabetic retinopathy. Guidelines recommend annual eye exams for those with diabetes to monitor for retinopathy and referrals for proliferative retinopathy or other complications.
Acute Rise in IOP (Dr. Rasha, senior resident of ophthalmology)Hind Safwat
There are several potential causes of acute increases in intraocular pressure (IOP), including glaucomatocyclitic crisis (Posner-Schlossman syndrome), inflammatory open-angle glaucoma, retrobulbar hemorrhage or inflammation, traumatic glaucoma, pigmentary glaucoma, neovascular glaucoma, plateau iris syndrome, and malignant glaucoma. IOP increases above 40mmHg can rapidly damage the optic nerve and cause permanent vision loss within hours. Treatment depends on the underlying cause but generally involves topical medications to lower IOP such as beta-blockers, alpha-2 agonists, and carbonic anhydrase inhibitors as well as systemic therapies like oral acetazol
"The image shows no signs of diabetic macular edema."
If exudates are found between 1DD to 2DD from the fovea, it is classified as less
significant stage. The
The document discusses diabetic retinopathy, including its definition, signs and symptoms, causes, risk factors, stages, treatments including laser photocoagulation and intravitreal injections, and importance of glycemic and blood pressure control. It emphasizes the need for regular eye exams in people with diabetes to screen for and treat diabetic eye diseases early.
Detection of eye disorders through retinal image analysisRahul Dey
This document describes methods for detecting eye disorders through retinal image analysis. It discusses segmenting blood vessels and the optic disk using algorithms. It also covers applying fuzzy logic image processing to enhance edge detection of blood vessels. The proposed approach uses a Mamdani fuzzy inference system on a moving window to classify edges based on gradient inputs and Gaussian membership functions. Simulation results show the fuzzy method enhances edge detection compared to common methods like Canny, Sobel, and Prewitt.
The Advanced Glaucoma Intervention Study was a multicenter randomized clinical trial that compared two sequences of surgical treatment for advanced glaucoma. It involved 789 eyes of 591 patients followed over 13 years. The study found that lower IOP was associated with less visual field progression. It also found differences in outcomes between black and white patients and between the two treatment sequences of initial trabeculectomy versus initial augmented trabeculotomy. The study provided important information about risk factors for surgical failure and progression of visual loss in advanced glaucoma.
This document discusses a study on knowledge and awareness of diabetic eye conditions among type 2 diabetic patients in Buraidah, Saudi Arabia. The study aimed to assess patients' baseline knowledge using a questionnaire, provide an educational booklet, and re-assess knowledge. Results showed that after the intervention, patients' knowledge increased significantly from 51% correct pre-intervention to 86% post-intervention. The study highlights the importance of education in improving diabetic patients' understanding of eye complications to facilitate early detection and treatment.
This document summarizes a seminar on assessing the visual field using automated perimetry. It defines key terminology used in visual field testing like threshold, apostilbs, decibels, and indices. It describes the components and functioning of the Humphrey Field Analyzer automated perimeter. It provides criteria for diagnosing glaucoma based on visual field tests and categorizes the severity of visual field defects as early, moderate, or severe. It also outlines how to recognize progressive damage by comparing tests over time.
The Ocular Hypertension Treatment Study (OHTS) was a landmark randomized controlled trial that showed treating patients with ocular hypertension reduced the risk of developing primary open-angle glaucoma by more than 50% compared to observation alone. Increased risk factors for developing glaucoma included older age, larger cup-to-disc ratios, higher baseline intraocular pressure, and thinner central corneal thickness. The Early Manifest Glaucoma Trial found that treating newly diagnosed glaucoma patients lowered their intraocular pressure by 25% on average and reduced the risk of visual field progression by about 20% compared to no treatment. The Collaborative Initial Glaucoma Treatment Study found that both medical and surgical treatment were effective for initially lowering intra
Diabetic retinopathy is caused by chronic hyperglycemia leading to progressive dysfunction of the retinal vasculature. This causes vascular leakage, focal ischemia, retinal hypoxia and neovascularization as well as thickening of the basement membrane and loss of pericytes impairing oxygen and nutrient flow. The stages include non-proliferative diabetic retinopathy characterized by microaneurysms and hemorrhages, and proliferative diabetic retinopathy characterized by new vessel growth. Macular edema can also occur from fluid leakage causing vision loss.
OCT-Angiography is a non-invasive imaging method that uses light to visualize the retinal and choroidal vasculature in 3D without dye injection. It works by detecting the movement of red blood cells on sequential OCT scans to identify blood vessels. The document describes the technical aspects and clinical applications of several commercial OCT-Angiography systems.
Landuse Classification from Satellite Imagery using Deep LearningDataWorks Summit
With the abundance of remote sensing satellite imagery, the possibilities are endless as to the kind of insights that can be derived from them. One such use is to determine land use for agriculture and non-agricultural purposes.
In this talk, we’ll be looking at leveraging Sentinel-2 satellite imagery data along with OpenStreetMap labels to be able to classify land use as agricultural or non-agricultural.
Sentinel-2 data has a 10-meter resolution in RGB bands and is well-suited for land use classification. Using these two datasets, many different machine learning tasks can be performed like image segmentation into two classes (farm land and non-farm land) or more challenging task of identification of crop type being cultivated on fields.
For this talk, we’ll be looking at leveraging convolutional neural networks (CNNs) built with Apache MXNet to train deep learning models for land use classification. We’ll be covering the different deep learning architectures considered for this particular use case along with the appropriate metrics.
We’ll be leveraging streaming pipelines built on Apache Flink and Apache NiFi for model training and inference. Developers will come away with a better understanding of how to analyze satellite imagery and the different deep learning architectures along with their pros/cons when analyzing satellite imagery for land use. SUNEEL MARTHI and CHRIS OLIVIER, Software Development Engineer Amazon Web Services
This document discusses diabetic retinopathy, including:
- The two main types of diabetes and how they relate to retinopathy risk and onset age.
- Diabetic retinopathy as a leading cause of blindness and its impact.
- Key risk factors like diabetes duration, glycemic control, and other systemic factors.
- The characteristic lesions and stages of non-proliferative and proliferative retinopathy.
- Treatment approaches including laser photocoagulation, anti-VEGF injections, steroids, and surgery.
- Screening guidelines based on diabetes type and risk level.
Smart Presentation Control by Hand Gestures Using Computer Vision and Google’...IRJET Journal
This document describes a smart presentation control system using hand gesture recognition with computer vision and Google's MediaPipe framework. The system uses a webcam to capture videos and photos of hand gestures as input. MediaPipe is used to detect hand landmarks and gestures in real-time. Various hand gestures like changing slides, drawing on slides, and erasing can be used to control the presentation without needing a keyboard or mouse. The system aims to provide a natural and intuitive human-computer interaction experience for presentation control through hand gesture recognition.
Automatic Detection of Diabetic Maculopathy from Fundus Images Using Image An...Eman Al-dhaher
Diabetic retinopathy is a severe eye disease that affects many diabetic patients. It changes the small blood vessels in the retina resulting in loss of vision. Early detection and diagnosis have been identified as one of the ways to achieve a reduction in the percentage of visual impairment and blindness caused by diabetic retinopathy with emphasis on regular screening for detection and monitoring of this disease.
The work focuses on developing a fundus image analysis system that extracts the fundal features of the retina such as optic disk, macula (i.e., fovea) and exudates lesions (hard and soft exudates), which are the fundamental steps in an automated analyzing system to display and diagnosis diabetic retinopathy.
Automatic Blood Vessels Segmentation of Retinal ImagesHarish Rajula
The document discusses automatic segmentation of blood vessels in retinal images. It presents a proposed system that uses morphological operations and an SVM classifier for blood vessel segmentation. The system first enhances retinal images using histogram equalization. It then processes the green channel using morphological operations like dilation and erosion. Features are extracted from the processed image and used to train an SVM classifier to detect and segment blood vessels. The proposed method achieved an average sensitivity of 78%, specificity of 97.99%, and accuracy of 99.6% on retinal images.
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.
Diabetic retinopathy is a leading cause of blindness worldwide. Prolonged hyperglycemia can damage retinal blood vessels and neurons. Over time, this can lead to vision loss through retinal edema, hemorrhage, fibrosis or neovascularization. Risk factors include duration and control of diabetes, hypertension, and nephropathy. Treatment focuses on laser photocoagulation and intravitreal injections to reduce edema or abnormal blood vessels, along with glycemic control to prevent progression. Regular screening is important to detect diabetic retinopathy early when treatment is most effective.
Review of methods for diabetic retinopathy detection and severity classificationeSAT Journals
Abstract Diabetic Retinopathy is a serious vascular disorder that might lead to complete blindness. Therefore, the early detection and the treatment are necessary to prevent major vision loss. Though the Manual screening methods are available, they are time consuming and inefficient on a large image database of patients. Moreover, it demands skilled professionals for the diagnosis. Automatic Diabetic Retinopathy diagnosis systems can replace manual methods as they can significantly reduce the manual labor involved in the screening process. Screening conducted over a larger population can become efficient if the system can separate normal and abnormal cases, instead of the manual examination of all images. Therefore, Automatic Retinopathy detection systems have attracted large popularity in the recent times. Automatic retinopathy detection systems employ image processing and computer vision techniques to detect different anomalies associated with retinopathy. This paper reviews various methods of diabetic retinopathy detection and classification into different stages based on severity levels and also, various image databases used for the research purpose are discussed. Keywords— Automatic Diabetic Retinopathy detection, computer vision, Diabetic Retinopathy, image databases, image processing, manual screening
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.
This document discusses diabetic retinopathy, which is the progressive dysfunction of the retinal blood vessels caused by chronic hyperglycemia. It defines the condition and stages of diabetic retinopathy, from mild non-proliferative to severe proliferative retinopathy. Risk factors include high blood sugar, hypertension, and hyperlipidemia. The document also covers diagnosing, treating, and preventing diabetic retinopathy through strict glycemic control and annual eye exams.
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.
Automated feature extraction for early detection of diabetic retinopathy immanish91
This document describes an automated method for detecting features in retinal images that can help diagnose diabetic retinopathy. It first extracts blood vessels at multiple scales using morphological operations. It then detects exudates by finding bright regions with sharp edges using dilation across scales. The optic disk is localized by finding the intersection of major blood vessels. Microaneurysms and hemorrhages are detected using morphological filters exploiting their local dark patch property. Evaluation on 516 images achieved 97.1% optic disk localization, 95.7% sensitivity and 94.2% specificity for exudate detection, and 95.1% sensitivity and 90.5% specificity for microaneurysm/hemorrhage detection.
- Diabetic retinopathy is a leading cause of blindness in people aged 20-74 and affects individuals in their most productive years. The risk and severity of retinopathy increases with longer duration of diabetes and poorer blood glucose control.
- The disease involves microvascular changes including microaneurysms, hemorrhages, hard exudates, and new abnormal blood vessel growth. Without treatment, this can lead to vision loss from macular edema, retinal detachment, or vitreous hemorrhage.
- Treatment involves managing blood sugar and blood pressure, as well as laser photocoagulation surgery or intravitreal injections to prevent vision loss from proliferative retinopathy or macular edema.
This document discusses diabetic retinopathy, its causes, stages, treatments, and prevention. It is progressive retinal vessel dysfunction caused by long-term hyperglycemia. Key factors that contribute to its development include hypertension, hyperlipidemia, female sex, pregnancy, smoking, obesity, and poor metabolic control. Stages include non-proliferative and proliferative retinopathy. Treatments include anti-VEGF drugs, laser photocoagulation, vitrectomy, and strict control of blood sugar and blood pressure to prevent its progression.
Autogenous Diabetic Retinopathy Censor for Ophthalmologists - AKSHIAsiri Wijesinghe
Full-fledged autogenous censor for classifying severity level of Diabetic Retinopathy (based on retinal lesions) and detecting retinal vascular network to assessment of vessel tortuousness to identify abnormal vessels in human retina.
Performance analysis of retinal image blood vessel segmentationacijjournal
The retinal image diagnosis
is an important methodology for diabetic retinopathy detection and analysis. in
this paper, the morphological operations and svm classifier are used to detect and segment the blood
vessels from the retinal image. the proposed system consists of three stage
s
-
first is preprocessing of retinal
image to separate the green channel and second stage is retinal image enhancement and third stage is
blood vessel segmentation using morphological operations and svm classifier. the performance of the
proposed system is
analyzed using publicly available dataset
1. The document proposes a new approach for detecting microaneurysms in retinal images that combines multiple preprocessing methods and candidate extraction techniques before classification.
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3. Experimental results show the proposed combining approach outperforms individual candidate extraction methods for microaneurysm detection.
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.
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.
Automatic identification and classification of microaneurysms for detection o...eSAT Journals
Abstract Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction. He experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards him from the opposite direction. We are expected to dim the headlight to avoid this glare. This glare causes a temporary blindness to a person resulting in road accidents during the night. To avoid such incidents, we have fabricated a prototype of automatic headlight dimmer. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. It also eliminates the requirement of manual switching by the driver which is not done at all times. The construction, working and the advantages of this prototype model is discussed in detail in this paper. Keywords: Headlight, automatic, dimmer, control, high beam, low beam, Kelvin (K).
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.
Detection of Exudates Caused By Diabetic Retinopathy in Fundus Retinal Image ...IOSR Journals
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AWS re:Invent 2016: Automatic Grading of Diabetic Retinopathy through Deep Le...Amazon Web Services
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This document discusses graphics programming and concepts such as viewing, transformations, projection, animation, and examples. It covers translation, rotation, and scaling transformations, as well as viewing transformations like gluLookAt. It also discusses projection transformations including perspective and orthographic projection. Finally, it provides examples of animating a spinning rectangle and building a solar system and robot arm.
This document discusses debugging multiple exceptions that occur across threads. It presents code with two threads that each cause an exception by dereferencing a null pointer. The crash report shows that thread 2 crashed with an EXC_BAD_ACCESS exception. GDB output shows examining the crashed thread 2 and disassembling the code where it crashed.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
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3. Common block diagram reduction techniques include combining blocks in cascade or parallel, moving summing or pickoff points, eliminating feedback loops, and swapping summing points.
This document discusses 2D graphics programming in OpenGL. It covers primitive types like points, lines, triangles, and polygons. It describes how to specify vertices to draw these primitive types and how to set properties like color, line width, and stippling. The RGB color space and methods for specifying color and shading models are also summarized.
A general introduction to GPGPU and an application involving solving large preconditioning problems with Domain Decomposition. Code is available at http://sourceforge.net/projects/cudasolver/ .
Evolutionary Algorithms and their Applications in Civil Engineering - 1shreymodi
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The document discusses the chain rule, which is used to find the derivative of a composite function. It defines a composite function as one made up of layers of functions inside other functions. The chain rule states that for a function y=f(g(x)), the derivative is equal to the derivative of the outer function multiplied by the derivative of the inner function. Several examples are provided to demonstrate applying the chain rule to find the derivative of various composite functions step-by-step.
Variation and Quality (2.008x Lecture Slides)A. John Hart
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*Fundamentals of Manufacturing Processes on edX: https://www.edx.org/course/fundamentals-manufacturing-processes-mitx-2-008x
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LSGAN은 기존의 GAN loss가 아닌 MSE loss를 사용하여, 더욱 realistic한 데이터를 생성함.
LSGAN 논문 리뷰 및 PyTorch 기반의 구현.
[참고]
Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
A Fast Implicit Gaussian Curvature FilterYuanhao Gong
Minimizing Gaussian curvature is computationally expensive in traditional way. We present a new method that can minimize the Gaussian curvature without computing it. Our filter is 100 times faster than traditional solvers.
A new method of gridding for spot detection in microarray imagesAlexander Decker
This document presents a new method for spot detection in microarray images. It begins with edge detection using an adaptive multi-structure morphological algorithm to effectively suppress noise while preserving image edges. Morphological filling is then used to fill holes in the binary image output from edge detection. Finally, an automatic spot detection algorithm is used to segment each sub-grid into individual spot regions based on intensity projection profiles along the rows and columns of the sub-grid. Fuzzy c-means clustering is then applied to separate spots from background pixels in each segmented region. The results show the method is fully automatic without needing human intervention or parameter presetting.
A new method of gridding for spot detection in microarray imagesAlexander Decker
This document presents a new method for spot detection in microarray images. It begins with edge detection using an adaptive multi-structure morphological algorithm to effectively suppress noise while preserving image edges. Morphological filling is then used to fill holes in the binary image output from edge detection. Finally, an automatic spot detection algorithm is used to segment each sub-grid into individual spot regions by applying gridding based on the intensity projection profile of the sub-grid. Fuzzy c-means clustering is then used to segment each spot from the background pixels. The results show the method is fully automatic without needing human intervention or parameter presetting.
block diagram representation of control systemsAhmed Elmorsy
This document provides an introduction to block diagram representation of control systems. It discusses how block diagrams provide a pictorial representation of the relationships between elements in a system using blocks and arrows. The blocks represent system elements or operations, and the arrows represent the direction of signal or information flow. Specific topics covered include summing points, takeoff points, examples of representing equations as block diagrams, and canonical forms.
COMPARISON OF GPU AND FPGA HARDWARE ACCELERATION OF LANE DETECTION ALGORITHMsipij
The two fundamental components of a complete computer vision system are detection and classification.
The Lane detection algorithm, which is used in autonomous driving and smart vehicle systems, is within the
computer vision detection area. In a sophisticated road environment, lane marking is the responsibility of
the lane detection system. The warning system for a car that leaves its lane also heavily relies on lane
detection. The two primary stages of the implemented lane detection algorithm are edge detection and line
detection. In order to assess the trade-offs for latency, power consumption, and utilisation, we will
compare the state-of-the-art implementation performance attained with both FPGA and GPU in this work.
Our analysis highlights the benefits and drawbacks of the two systems.
Comparison of GPU and FPGA Hardware Acceleration of Lane Detection Algorithmsipij
The two fundamental components of a complete computer vision system are detection and classification.
The Lane detection algorithm, which is used in autonomous driving and smart vehicle systems, is within the
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the lane detection system. The warning system for a car that leaves its lane also heavily relies on lane
detection. The two primary stages of the implemented lane detection algorithm are edge detection and line
detection. In order to assess the trade-offs for latency, power consumption, and utilisation, we will
compare the state-of-the-art implementation performance attained with both FPGA and GPU in this work.
Our analysis highlights the benefits and drawbacks of the two systems.
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
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at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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4. Introduction
Figure 2: Diabetic macula edema
(swelling of the retina)
Diabetic retinopathy occurs when elevated blood sugar
levels cause blood vessels in the eye to swell and leak
into the retina.
4
5. Introduction
Abnormalities of Diabetic Retinopathy
•
•
•
•
Microaneurysms
Hemorphages
Cotton wool spots ( Soft Exudates)
Hard Exudates
Aim of this research is to develop system for detection
of hard exudates in diabetic retinopathy using nondilated diabetic retinopathy images
5
7. Methodology
Phase 1
Phase 2
Mathematical Morphology
Fuzzy Logic
• Exudates are identified
using mathematical
morphology
• Identified exudates are
classified as hard exudates
using fuzzy logic
7
9. Preprocessing
Input
Fundus Image
• Fundus Image is
performed by
fundus camera
Step 1
Step 2
Step 3
Step 4
Color Space
Conversion
Median
Filtering
Contrast
Enhancement
Gaussian
Filtering
• RGB color space
in the image in
converted to HIS
space
• Noise
suppression
• Contrast limited
adaptive
histogram
equalization was
applied for
contrast
enhancement
• Noise
Suppression
further
9
10. Optic Disc Elimination
Input
Preprocessed
Image
• Output of
preprocessing
stage
Step 1
Closing
• Closing operator
with flat disc
shape
structuring
element is
applied
Step 2
Step 3
Step 4
Thresholding
Large
Connected
component
Optic disc
elimination
• Image is
binarized
• P-tile method
and nilblack’s
method
• Connect all
regions
10
11. Exudates Detection
• Optic disc
eliminated
Image
• Standard
Deviation
• Remove optic
disc boundary
• Marker Image
• Difference
Image
I
n
p
u
t
• Closing
• Thresholding
• Fill holes
• Morphological
Reconstruction
• Result is
superimposed
11
15. Membership function of XB
Membership
function name
Parameters
[sig1 c1 sig2 c2]
B1
[0.217 0 3.081 5.408]
B2
[3 17 3 50]
B3
[3 60 3 102]
B4
[3 112 3 255]
Gaussian combination membership function
15
16. Membership function of Xout
Membership
function name
NotHardExudate
Parameters
[sig1 c1 sig2 c2]
[0.0008493 0 0.06795 0.07]
weakHardExudate [0.03 0.35 0.03 0.55]
mediumHardExud
ate
[0.03 0.65 0.03 0.75]
hardExudate
[0.03 0.85 0.03 0.9]
severeHardExudat [0.0161 0.9733 0.0256 1]
e
Gaussian combination membership function
16
17. Fuzzy rules
1
If (Xr is R1) Or (Xg is G1) Or (Xb is B4) Then (Xout is notHardExudate)
2
If (Xr is R2) And (Xg is G2) Or (Xb is B1) Then (Xout is weakHardExudate)
3
If (Xr is R2) And (Xg is Not G2) And (Xb is Not B1) Then (Xout is notHardExudate)
4
If (Xr is R3) And (Xg is G3) And ((Xb is B1) Or (Xb is B2) ) Then (Xout is weakHardExudate)
5
If (Xr is R3) And (Xg is G3) And (Xb is B3) Then (Xout is notHardExudate)
6
If (Xr is R3) And (Xg is Not G3) Then (Xout is notHardExudate)
7
If (Xr is R4) And (Xg is G3) And (Xb is B1) Then (Xout is mediumHardExudate)
8
If (Xr is R4) And (Xg is G3) And (Xb is B2) Then (Xout is weakHardExudate)
9
If (Xr is R4) And (Xg is Not G3) Then (Xout is notHardExudate)
10
If (Xr is R5) And ((Xg is G2) Or (Xg is G3) Or (Xg is G4)) Then (Xout is notHardExudate)
11
If (Xr is R5) And (Xg is G5) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
12
If (Xr is R5) And ((Xg is G6) Or (Xg is G7)) Then (Xout is notHardExudate)
13
If (Xr is R5) And (Xb is B3) Then (Xout is notHardExudate)
14
If (Xr is R6) And ((Xg is G2) Or (Xg is G3)) Then (Xout is notHardExudate)
15
If (Xr is R6) And (Xg is G4) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
16
If (Xr is R6) And (Xg is G5) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
17
If (Xr is R6) And (Xg is G6) And ((Xb is B1) Or (Xb is B2)) Then (Xout is HardExudate)
18
If (Xr is R6) And (Xg is G7) Then (Xout is notHardExudate)
19
If (Xr is R6) And (Xb is B3) Then (Xout is notHardExudate)
20
If (Xr is R7) And (Xg is G6) And ((Xb is B1) Or (Xb is B2) Or (Xb is B3)) Then (Xout is severeHardExudate)
21
If (Xr is R7) And (Xg is G5) And ((Xb is B1) Or (Xb is B2)) Then (Xout is notHardExudate)
22
If (Xr is R7) And ((Xg is G2) Or (Xg is G3) Or (Xg is G4)) Then (Xout is notHardExudate)
17
18. Implementation
• 38 images were used to testing
• Images were taken from Kuopio university
hospital
• The images’ size were 1500 , 1152 pixels
Tested using MATLAB 7.10
18
20. Results – Optic Disc Elimination
(a)
(b)
(c)
(d)
(e)
(f)
(a)-Applying morphological closing operator, (b)-Thresholded image using
Nilblack’s method, (c)– Thresholded Image using percentile method,
(d)- Large circular connected component, (e)-Inverted binary image,
(f)- Optic disc is eliminated from the preprocessed image
20
21. Results – Exudates Detection
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(a)- Applying morphological closing operator , (b)- Standard deviation of the image ,
(c)-Thresholded image using triangle method , (d)- Unwanted borders were removed ,
(e)- Holes are flood filled , (f)- Marker Image , (g)- Morphological reconstructed image21
,
(h)- Thresholded image , (i)- Result is super imposed on original image
22. Results – Classification of Exudates
(a)
(b)
(c)
Performance
•
•
•
•
Overall sensitivity-81.76%
Specificity – 99.96%
Precision – 81%
Accuracy – 99.84%
(a)- Not exist diabetic retinopathy,
(b)- 42% of diabetic retinopathy hard exudates ,
(c)- 89% of diabetic retinopathy hard exudates ,
22
23. Future Works
•
•
•
•
Preprocessing Stage
Optic Disc Elimination
Exudates Detection
Classification of Exudates as Hard
Exudates
• Exudative Maculopathy Detection
• Support Vector Machines, K Means
Algorithms, Radial Basis Functions
Tested using MATLAB 7.10
23
26. References
• Meysam Tavakoli, Reza Pourreza Shahri, Hamidreza Pourreza, Alireza Mehdizadeh,
Touka Banaee, Mohammad Hosein Bahreini Toosi, A complementary method for
automated detection of microaneurysms in fluorescein angiography fundus images
to assess diabetic retinopathy, Pattern Recognition, Volume 46, Issue 10, October
2013, Pages 2740-2753, ISSN 0031-3203,
http://dx.doi.org/10.1016/j.patcog.2013.03.011.
(http://www.sciencedirect.com/science/article/pii/S0031320313001404)
• M. Usman Akram, Shehzad Khalid, Shoab A. Khan, Identification and classification
of microaneurysms for early detection of diabetic retinopathy, Pattern Recognition,
Volume 46, Issue 1, January 2013, Pages 107-116, ISSN 0031-3203,
http://dx.doi.org/10.1016/j.patcog.2012.07.002.
(http://www.sciencedirect.com/science/article/pii/S003132031200297X)
• R.H.N.G. Ranamuka, Automatic detection of diabetic retinopathy hard exudates
using mathematical morphology methods and fuzzy logic, Graduation Thesis,
University of Sri Jayewardenepura, 2011
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Microaneursms is the early stage of Diabetic Retinopathy
Akara’s suggested certain steps for optic disc detection prior to the exudates identification.After the optic disc elimination mathematical morphology has been used for the exudates detectionAkara proposed a Fuzzy C Means (FCM) clustering method for exudates detectionSuggested a computer based approach for automated classification of Normal, NPDR and DPRThey have used the green layer for exudates detection because they have discovered that the brightness area including exudates of retinal image is in green layer in literature
Intensity band of the HIS image is used at this stageFirstly RGB color space in the original fundus image is converted to HIS (HUE, Intensity and saturation) space.Then median filter is applied for the intensity band of the image for the noise suppression. Median filter is non linear median filter which is used to remove noises in an image with minimal degradation to edges.Subsequently the Contrast limited adaptive histogram equalization was applied for contrast enhancement this adaptive histogram method is used to improve the local contrast of an image. It may be produce a significant noiseGaussian function is applied for noise suppression furtherThis gaussian filtering function is used to filter out the noise in the image without compromising on the region of interest
Firstly the closing operator with a flat disk shape structuring element is applied for the preprocessed image.Then the result image is binarized using thresholdingtechniqueClosing is a morphological operation
Remove optic disc boundaryTriangle method is used to obtain thesholded imageFill HolesMarker image The intensity band of original image is selected as the mask image.Morphological ReconstructionDifference Image
I have used the RGB color space values of retinal image to form the fuzzy set and the membership functionsNot Hard ExudatesWeak Hard ExudatesMedium Hard ExudatesHard ExudatesSevere Hard Exduates
There are 7 linguistic variables for Xr
There are 7 linguistic variables for Xg
There are 4 linguistic variables for Xr
There are 5 linguistic variables for output value
Fuzzy rules
Those 38 images were publicly available diabetic retinopathy dataset
Those 38 images were publicly available diabetic retinopathy dataset
Proposed Radon Transform method to detect MAs
ProposedHybrid Classifier which combines Gaussian Mixture Model and Support Vector Machine
Those 38 images were publicly available diabetic retinopathy dataset