scholarly journals Detection of Hard Exudates Based on Morphological Feature Extraction

2018 ◽  
Vol 11 (1) ◽  
pp. 215-225 ◽  
Author(s):  
Shilpa Joshi ◽  
P. T. Karule
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Morphological Feature ◽  
Vision Loss ◽  
Automated System ◽  
Diabetic Patients ◽  
Computationally Efficient ◽  
Image Processing Algorithm ◽  
Hard Exudates ◽  
Processing Techniques

In diabetic patients, the chances of vision loss are higher. These issues related to vision can be diagnosed using diabetic retinopathy. It is one of the very important diseases amongst all retinal pathologies. One of the simplest changes observed on the eye due to diabetes is lesions in yellow or white color i.e. hard exudates (EX). It appears bright in fundus images and hence it is the most important to detect using image processing algorithm. In this work the proposed algorithm used is based on morphological feature extraction. Post processing techniques are required to separate out EX from other bright artefacts such as cotton wool spot and optic disc. The performance evaluation of the proposed algorithm shows the sensitivity of 96.7%, specificity 85.4% and accuracy of 91% on image level detection on Diaretdb1 database and achieved higher accuracy on publicly available e-ophtha EX retinal image database in terms of lesion level detection. It is computationally efficient as an automated system to assist the ophthalmologist. Early detection of hard exudates is crucial for diagnosing the stages of diabetic retinopathy to prevent blindness.

scholarly journals Accuracy Enhancement of Diabetic Retinopathy Detection Using Naive Bayes Algorithm

2021 ◽  
Vol 9 (VII) ◽  
pp. 3648-3652
Author(s):  
Mithileshkumar Yadav
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Automated System ◽  
Diabetic Patients ◽  
Detection Accuracy ◽  
Monitoring Model ◽  
Bayes Algorithm ◽  
Retinal Fundus

Diabetic retinopathy (DR) is a disease of eye which is caused by diabetes. Sometime the DR leads the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to protect the eyesight and provide help for timely treatment. The detection of DR can be done manually by ophthalmologists and can also be done by an automated system. An ophthalmologist is required to analyze and explain retinal fundus images in the manual system, which is a time consuming and very expensive task. While, In the automated system, artificial intelligence is used to perform an significant role in the area of ophthalmology and specifically in the early detection of DR over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported, But still research for accurate detection of DR is going on. In this paper, a new diabetic retinopathy monitoring model is proposed by using the Naive Bayes method to improve the accuracy of detection of DR. The model is trained on mixture of two datasets Messidor and Kaggle, and evaluated on the Messidor dataset. By using proposed method detection accuracy is found to be higher than existing methods.

scholarly journals Harnessing Feature Extraction Techniques alongside CNN for Diabetic Retinopathy Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 422-425
Keyword(s):  
Diabetes Mellitus ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Medical Condition ◽  
Machine Learning Algorithms ◽  
Extraction Techniques ◽  
Energy Deficiency ◽  
Automated Screening ◽  
Processing Techniques

Diabetes mellitus is a disorder that inhibits your body from properly using the energy from the food you consume. The blood vessels and blood are responsible for the transport of sugar. A hormone called insulin helps cells to take in sugar to be used as energy. Deficiency in insulin causes the disease of diabetes mellitus. One of the side effect of diabetes mellitus is diabetic retinopathy. Diabetic retinopathy is the medical condition that causes the principal vision or in rare cases entire vision loss. Diabetic retinopathy has frequent occurrences in people among 20 to 60 years. Addressing this problem, we have developed an application that saves time and gives the result of the stage of the disease. This research paper presents a CNN based system that classifies the patients in four classes as 0-no DR, 1-Mild DR, 2-Moderate DR, 3-Severe DR. The system takes the input as an image taken from a fundus camera. Image processing techniques and machine learning algorithms are used for feature extraction. The Automated screening of the retinal images would assist the doctors to easily identify the patient's condition more precisely. With this we can easily distinguish between normal and abnormal images of the retina, this will reduce the number of inspections for the doctors.

scholarly journals Plasma microRNA signature associated with retinopathy in patients with type 2 diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Donato Santovito ◽  
Lisa Toto ◽  
Velia De Nardis ◽  
Pamela Marcantonio ◽  
Rossella D’Aloisio ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
External Validation ◽  
Diabetic Patients ◽  
Disease Biomarkers ◽  
Response To Stress ◽  
Severity Of The Disease ◽  
Plasma Microrna

AbstractDiabetic retinopathy (DR) is a leading cause of vision loss and disability. Effective management of DR depends on prompt treatment and would benefit from biomarkers for screening and pre-symptomatic detection of retinopathy in diabetic patients. MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression which are released in the bloodstream and may serve as biomarkers. Little is known on circulating miRNAs in patients with type 2 diabetes (T2DM) and DR. Here we show that DR is associated with higher circulating miR-25-3p (P = 0.004) and miR-320b (P = 0.011) and lower levels of miR-495-3p (P < 0.001) in a cohort of patients with T2DM with DR (n = 20), compared with diabetic subjects without DR (n = 10) and healthy individuals (n = 10). These associations persisted significant after adjustment for age, gender, and HbA1c. The circulating levels of these miRNAs correlated with severity of the disease and their concomitant evaluation showed high accuracy for identifying DR (AUROC = 0.93; P < 0.001). Gene ontology analysis of validated targets revealed enrichment in pathways such as regulation of metabolic process (P = 1.5 × 10–20), of cell response to stress (P = 1.9 × 10–14), and development of blood vessels (P = 2.7 × 10–14). Pending external validation, we anticipate that these miRNAs may serve as putative disease biomarkers and highlight novel molecular targets for improving care of patients with diabetic retinopathy.

scholarly journals Utilization of diabetic retinopathy screening among diabetic patients at a tertiary care hospital in Kerala, India

2021 ◽  
Vol 8 (10) ◽  
pp. 4938
Author(s):  
Sona Sabitha Kumar ◽  
Lathika Vasu Kamaladevi ◽  
Sruthi Mankara Valsan
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Adult Population ◽  
Tertiary Care ◽  
Health Concern ◽  
Diabetic Patients ◽  
Care Hospital ◽  
Screening Programs ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

Background: Diabetes is a major public health concern that affects nearly 463 million (9.3%) of global adult population. Diabetic retinopathy, which affects around 35% of all diabetic patients, is the fifth leading cause of preventable global blindness. This study was done to determine the status of diabetic retinopathy screening and the factors that influence its uptake among diabetic patients attending a tertiary care setting in Kerala, India.Methods: 200 patients with diabetes mellitus on physician care were enrolled for a questionnaire-based survey which collected information on patient demographics, education, occupation, patient’s awareness of retinopathy, screening, diabetic blindness and their source of such knowledge.Results: 83% were aware that diabetes can result in vision loss. 61% were aware that diabetic blindness is preventable. 42% patients were aware of screening options for retinopathy. The awareness of retinopathy screening was significantly associated (p=0.0001) only with duration of diabetes.Conclusions: Awareness of diabetic retinopathy among diabetic patients in Kerala was sub optimal. Better patient education and use of mass media can increase awareness on diabetes retinopathy screening programs. 

scholarly journals Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6549
Author(s):  
Roberto Romero-Oraá ◽  
María García ◽  
Javier Oraá-Pérez ◽  
María I. López-Gálvez ◽  
Roberto Hornero
Keyword(s):  
Diabetic Retinopathy ◽  
Lesion Detection ◽  
Computer Assisted ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Public Database ◽  
Diagnostic Systems ◽  
Hard Exudates ◽  
The Public ◽  
Proprietary Database

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

scholarly journals Reduction of Glut1 in retinal neurons but not the RPE alleviates polyol accumulation and normalizes early characteristics of diabetic retinopathy

2020 ◽  
Author(s):  
Nicholas C. Holoman ◽  
Jacob J. Aiello ◽  
Timothy D. Trobenter ◽  
Matthew J. Tarchick ◽  
Michael R. Kozlowski ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Diabetic Retinopathy ◽  
Aldose Reductase ◽  
Glucose Transporter ◽  
Vision Loss ◽  
Significant Proportion ◽  
Diabetic Patients ◽  
Retinal Neurons ◽  
Anti Vegf ◽  
Facilitative Glucose Transporter

AbstractHyperglycemia is a key determinant for development of diabetic retinopathy (DR). Inadequate glycemic control exacerbates retinopathy, while normalization of glucose levels delays its progression. In hyperglycemia, hexokinase is saturated and excess glucose is metabolized to sorbitol by aldose reductase via the polyol pathway. Therapies to reduce retinal polyol accumulation for the prevention of DR have been elusive due to low sorbitol dehydrogenase levels in the retina and inadequate inhibition of aldose reductase. Using systemic and conditional genetic inactivation, we targeted the primary facilitative glucose transporter in the retina, Glut1, as a preventative therapeutic in diabetic male and female mice. Unlike wildtype diabetics, diabetic Glut1+/− mice did not display elevated Glut1 levels in the retina. Furthermore, diabetic Glut1+/− mice exhibited ameliorated ERG defects, inflammation and oxidative stress, which was correlated with a significant reduction in retinal sorbitol accumulation. RPE-specific reduction of Glut1 did not prevent an increase in retinal sorbitol content or early hallmarks of DR. However, like diabetic Glut1+/− mice, reduction of Glut1 specifically in retinal neurons mitigated polyol accumulation and completely prevented retinal dysfunction and the elevation of markers for oxidative stress and inflammation associated with diabetes. These results suggest that modulation of retinal polyol accumulation via Glut1 in photoreceptors can circumvent the difficulties in regulating systemic glucose metabolism and be exploited to prevent DR.SignificanceDiabetic retinopathy (DR) affects one third of diabetic patients and is the primary cause of vision loss in adults aged 20-74. While anti-VEGF and photocoagulation treatments for the late-stage vision threatening complications can prevent vision loss, a significant proportion of patients do not respond to anti-VEGF therapies and mechanisms to stop progression of early-stage symptoms remain elusive. Glut1 is the primary facilitative glucose transporter for the retina. We determined that a moderate reduction in Glut1 levels, specifically in retinal neurons, but not the RPE, was sufficient to prevent retinal polyol accumulation and the earliest functional defects to be identified in the diabetic retina. Our study defines modulation of Glut1 in retinal neurons as a targetable molecule for prevention of DR.

scholarly journals Detection of Diabetic Retinopathy by Retinal Screening using Image Processing Techniques

2020 ◽  
pp. 447-457
Author(s):  
Shaziya Banu S ◽  
Ravindra S
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Nearest Neighbor ◽  
Extreme Condition ◽  
Diabetic Patients ◽  
K Nearest Neighbor ◽  
Image Processing Techniques ◽  
Primary Driver ◽  
Processing Techniques

<p>Diabetic Retinopathy (DR) is a related malady with diabetes and primary driver of sightlessness in diabetic patients. Epidemiological overview categorizes DR among four significant reasons for sight impedance. DR is a microvascular entanglement in which meager retinal veins may blast, bringing about vision misfortune. In this condition veins in retina swells and may blast in severe extreme condition. Operative medication is timely discovery by steady screenings that is by emphasizing the determination of retinal images using appropriate image processing techniques such as, Preprocessing of retinal image, image segmentation using sobel edge detector, local features extraction like mean, standard deviation, variance, Entropy, histogram values and so on. For classification of retina, system uses K-Nearest Neighbor (KNN) classifier. By adopting this approach, The classification of normal and abnormal images of retina is easy and will reduce the number of reviews for the ophthalmologists. Developing a method to automate functionality of retinal examination helps doctor to identify patient’s condition on disease. So that they can medicate the disease accordingly.</p>

scholarly journals Screening for Diabetic Retinopathy in Endocrinology Clinics by Using Handheld Cameras and Applying Artificial Intelligence Algorithms

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A419-A420
Author(s):  
Zack Dvey-Aharon ◽  
Petri Huhtinen
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Ground Truth ◽  
World Health ◽  
Diabetic Patients ◽  
Early Screening ◽  
Exclusion Criteria ◽  
Fundus Camera ◽  
The World

Abstract According to estimations of the World Health Organization (WHO), there are almost 500M people in the world that suffer from diabetes. Projections suggest this number will surpass 700M by 2045 with global prevalence surpassing 7%. This huge population, alongside people with pre-diabetics, is prone to develop diabetic retinopathy, the leading cause of vision loss in the working age. While early screening can help prevent most cases of vision loss caused by diabetic retinopathy, the vast majority of patients are not being screened periodically as the guidelines instruct. The challenge is to find a reliable and convenient method to screen patients so that efficacy in detection of referral diabetic retinopathy is sufficient while integration with the flow of care is smooth, easy, simple, and cost-efficient. In this research, we described a screening process for more-than-mild retinopathy through the application of artificial intelligence (AI) algorithms on images obtained by a portable, handheld fundus camera. 156 patients were screened for mtmDR indication. Four images were taken per patient, two macula centered and two optic disc centered. The 624 images were taken using the Optomed Aurora fundus camera and were uploaded using Optomed Direct-Upload. Fully blinded and independently, a certified, experienced ophthalmologist (contracted by Optomed and based in Finland) reviewed each patient to determine ground truth. Indications that are different than mtmDR were also documented by the ophthalmologist to meet exclusion criteria. Data was obtained from anonymized images uploaded to the cloud-based AEYE-DS system and analysis results from the AI algorithm were promptly returned to the users. Of the 156 patients, a certified ophthalmologist determined 100% reached sufficient quality of images for grading, and 36 had existing retinal diseases that fall under exclusion criteria, thus, 77% of the participants met the participation criteria. Of the remaining 120 patients, the AEYE-DS system determined that 2 patients had at least one insufficient quality image. AEYE-DS provided readings for each of the 118 remaining patients (98.3% of all patients). These were statistically compared to the output of the ground truth arm. The patient ground truth was defined as the most severe diagnosis from the four patient images; the ophthalmologist diagnosed 54 patients as mtmDR+ (45% prevalence). Of the 54 patients with referable DR, 50 were diagnosed and of the 64 mtmDR- patients, 61 were correctly diagnosed by the AI. In summary, the results of the study in terms of sensitivity and specificity were 92.6% and 95.3%, respectively. The results indicated accurate classification of diabetic patients that required referral to the ophthalmologist and those who did not. The results also demonstrated the potential of efficient screening and easy workflow integration into points of care such as endocrinology clinics.

scholarly journals An Effective Method for Detecting and Classifying Diabetic Retinopathy Lesions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Abdüssamed Erciyas ◽  
Necaattin Barışçı
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Vision Loss ◽  
Diabetic Patients ◽  
Disease Detection ◽  
Early Vision ◽  
Second Stage ◽  
22Nd Century ◽  
Harmful Effects

Diabetic retinopathy occurs as a result of the harmful effects of diabetes on the eyes. Diabetic retinopathy is also a disease that should be diagnosed early. If not treated early, vision loss may occur. It is estimated that one third of more than half a million diabetic patients will have diabetic retinopathy by the 22nd century. Many effective methods have been proposed for disease detection with deep learning. In this study, unlike other studies, a deep learning-based method has been proposed in which diabetic retinopathy lesions are detected automatically and independently of datasets, and the detected lesions are classified. In the first stage of the proposed method, a data pool is created by collecting diabetic retinopathy data from different datasets. With Faster RCNN, lesions are detected, and the region of interests are marked. The images obtained in the second stage are classified using the transfer learning and attention mechanism. The method tested in Kaggle and MESSIDOR datasets reached 99.1% and 100% ACC and 99.9% and 100% AUC, respectively. When the obtained results are compared with other results in the literature, it is seen that more successful results are obtained.

scholarly journals An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

2018 ◽  
Vol 7 (2.15) ◽  
pp. 154 ◽  
Author(s):  
Fanji Ari Mukti ◽  
C Eswaran ◽  
Noramiza Hashim ◽  
Ho Chiung Ching ◽  
Mohamed Uvaze Ahamed Ayoobkhan
Keyword(s):  
Diabetic Retinopathy ◽  
Hierarchical Classification ◽  
Curvelet Transform ◽  
Automated System ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Hard Exudates ◽  
Automated Grading ◽  
Fast Discrete Curvelet Transform

In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%. 

Sign in / Sign up

Export Citation Format

Share Document