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Objective: This study seeks to explain the relationship between systemic conditions and hard exudate formations in diabetic macular edema patients. Besides, the study aimed to quantitatively examine changes in the area, location, and impact on visual function of hard exudates following intravitreal dexamethasone implant injections. Methods: A retrospective analysis was conducted, including 40 patients (40 eyes) diagnosed with non-proliferative diabetic retinopathy and concurrent macular edema between January 1, 2022, and January 1, 2024. Preoperative evaluations included glycated hemoglobin, lipid profile, and renal function examinations. Based on the location of HE, patients were divided into two groups: Group A, with HE in 1 mm of the central fovea, and Group B, with HE outside 1 mm of the central fovea. Selected eyes were subject to pre- and postoperative examinations, including best-corrected visual acuity (BCVA), intraocular pressure, slit-lamp biomicroscopy, scanning laser ophthalmoscopy (SLO), optical coherence tomography, and multifocal electroretinography. Following screening and examination, patients received an immediate intravitreal injection of the DEX implant, with an injection administered at the four-month mark. Hard exudate (HE) areas were measured utilizing SLO fundus imaging. Results: Total cholesterol, low-density lipoprotein, and triglyceride levels were found to be positively correlated with the presence of HE. Following surgical intervention, all patients demonstrated an improvement in BCVA. The mean BCVA increased from a preoperative measurement of 0.79 ± 0.04 to 0.39 ± 0.02 at the 6 month follow-up, indicating a statistically significant difference (p < 0.001). The baseline HE area for the entire patient cohort was 2.28 ± 0.22. One month post-operation, the HE area exhibited a slight increase to 2.27 ± 0.22. However, by the 6 month follow-up, the HE area had significantly decreased to 0.8 ± 0.87, representing a 35.09% reduction from the baseline measurement (p < 0.001). It is worth noting that Patient P1 did not exhibit a statistically significant difference between preoperative and six-month postoperative HE area (p = 0.032). Preoperative BCVA measurements for Group A and Group B were 0.81 ± 0.03 and 0.77 ± 0.03, respectively, with no statistically significant intergroup difference (p = 0.333). The baseline HE area for Group A was 2.61 ± 0.16, which decreased to 0.38 ± 0.20 at the six-month follow-up, representing a 14.60% reduction from the baseline total area. For Group B, the baseline HE area was measured at 1.95 ± 0.09, then decreasing to 1.21 ± 0.13 at the six-month follow-up, indicating a 62.05% reduction from the baseline total area. A statistically significant difference in the postoperative 6 month HE area was observed between Group A and Group B (p < 0.001). In Group A, the reduction in HE area (initial HE area-final HE area) was positively correlated with the improvement in P1 (initial P1-final P1) (r = 0.610, p = 0.004). In Group B, a similar positive correlation was found (initial HE area-final HE area with initial P1-final P1) (r = 0.488, p = 0.029). In Group B, the reduction in HE area (initial HE area-final HE area) correlated positively with the improvement in BCVA (initial BCVA-final BCVA) (r = 0.615, p = 0.004). Additionally, in Group B, the reduction in HE area (initial HE area-final HE area) was positively correlated with the improvement in CMT (initial CMT-final CMT) (r = -0.725, p< 0.001). Aggravated cataracts were observed in thirteen eyes during a follow-up examination 6 months later. Conclusion: HE formation is associated with lipid levels. Dexamethasone implants demonstrate effectiveness in reducing HE areas in the short term, reducing macular edema, improving retinal structure, and enhancing visual function. The incidence of postoperative complications such as cataracts and glaucoma remains low. [ABSTRACT FROM AUTHOR]

Background: Dyslipidemia and hard exudates of the retina have an intricate association. Increased lipid levels cause increased hard exudates. Many studies found dyslipidemia as an independent risk factor for the development of hard exudates in maculopathy. Objectives: The objective of this study was the association between dyslipidemia and the presence of hard exudates among focal maculopathy patients with and without hard exudates. Materials and Methods: This was a hospital-based case–control study. This study included 60 patients with focal maculopathy in type 2 diabetes mellitus (DM) with hard exudates. Sixty patients with Focal maculopathy in type 2 DM without hard exudates were taken as control. Results: Dyslipidemia was significantly seen (P < 0.05) in cases compared to participants. Total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), and very LDL-C were significantly higher in cases compared to controls. High-density lipoprotein cholesterol (HDL-C) had the opposite outcome. HDL-C was significantly lower in cases compared to controls. Conclusion: Dyslipidemia was a strong risk factor for the formation of hard exudates in the macula. An increase in lipid levels increased the hard exudates concentration in the macula and retina.

Diabetic macular edema (DME) is a leading cause of vision impairment in diabetic patients, necessitating a timely and accurate diagnosis. This paper proposes a comprehensive system for DME grading using retinal fundus images. Our approach integrates multiple deep learning modules, each designed to address key aspects of the diagnostic process. The first module employs the ConvUNeXt model for segmenting hard exudates (HaEx), crucial indicators of DME. The second module uses RetinaNet for precise optic disc (OD) localization, which is essential for subsequent macula localization. The third module refines macula localization, leveraging preprocessing techniques to enhance image clarity. Finally, our system consolidates these results to provide interpretable DME grading. Experimental evaluations were conducted on the Messidor dataset, demonstrating the system's robust performance. The HaEx segmentation module achieved a mean IoU of 70.5% and a Dice coefficient of 0.64. The OD localization module showed perfect accuracy, recall, and precision at 1.0. For macula localization, our method satisfied the 1R criterion with 99.38% accuracy. The DME grading module achieved an overall accuracy of 91.12%, with an AUC of 0.9334. Our method offers a balanced performance across accuracy, sensitivity, and specificity compared to other non-interpretable and partially interpretable methods. [ABSTRACT FROM AUTHOR]

Purpose: Diabetic macular edema is one of the leading causes of vision loss across the world. Hard exudates at the macula can lead to structural abnormalities in the retina leading to irreversible vision loss. Systemic dyslipidemia and other modifiable risk factors when identified and treated early may help prevent substantial vision loss. The purpose of this study was to study the association between serum lipid levels and other systemic risk factors like hemoglobin, HbA1c, and serum creatinine with hard exudates and macular edema in patients with diabetic retinopathy. Methods: It is a prospective cross-sectional study conducted in a tertiary health care center in South India. 96 patients having diabetic retinopathy with hard exudates were included. Modified Airlie house classification was used to grade the hard exudates. Blood investigations including serum lipid profile, hemoglobin, HbA1c, and serum creatinine were carried out. Central subfield macular thickness was measured using optical coherence tomography. Results: 96 patients of type II DM with diabetic retinopathy were divided into three groups of hard exudates. A statistically significant correlation was observed between the severity of hard exudates and total cholesterol (p = 0.00), triglycerides (p = 0.00), LDL (p = 0.00), and VLDL (p = 0.00). HbA1c levels showed a statistically significant correlation with the severity of hard exudates (p = 0.09), no significant correlation was noted between hard exudates and hemoglobin levels (p = 0.27) and with serum creatinine (p = 0.612). A statistically significant association between CSMT and hard exudates (p = 0.00) was noted. Conclusion: In our study, we concluded that the severity of hard exudates is significantly associated with increasing levels of serum total cholesterol, triglycerides, LDL, VLDL, and HbA1c levels in type II DM patients presenting with diabetic retinopathy. The increasing duration of diabetes is significantly associated with increasing severity of hard exudates. Central subfield macular thickness increases with increasing severity of hard exudates in diabetic retinopathy. [ABSTRACT FROM AUTHOR]

Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive Jaya Optimization Algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) poses a major challenge in vision care, often leading to partial or complete limited vision in adults. Early and accurate detection of DR is essential for timely intervention and effective patient management. One critical aspect of DR diagnosis is identifying hard exudates in retinal images. Using deep tech or computer-aided methods, ophthalmologists can gain detailed insights into retinal health, facilitating precise diagnosis and treatment planning. However, the availability of medical image data in open datasets is often limited, making deep analysis challenging. To address this issue, we introduce a U-Net deep learning model, specifically modified and designed to segment hard exudates effectively, even with a limited dataset. In our study, the model is trained and tested on 89 original images from the DiaRetDB1 dataset, supplemented with augmented image to improve robustness. Despite the small dataset, our modified U-Net achieves high sensitivity, specificity, precision, and accuracy, culminating in an impressive F1 score of 0.9754. These results demonstrate the potential of our approach to improve DR diagnosis with limited resources. By adapting existing deep learning models, our research shows significant enhancements in diagnostic accuracy for DR. However, further validation with larger datasets and comparisons with other methods are necessary to confirm our findings. This study contributes to better detection of hard exudates in DR, ultimately leading to improved patient outcomes.

AIM:To investigate the influencing factors of abnormal telangiectasia secondary to diabetic retinopathy(DR).METHODS: Prospective studies. A total of 153 cases(240 eyes)with DR treated in our hospital from January 2021 to January 2023 were selected to analyze the risk factors of abnormal telangiectasia secondary to DR and its predictive efficacy.RESULTS: The patients were divided into dilated group(77 eyes of 40 cases)and non-dilated group(163 eyes of 113 cases)according to whether they had secondary abnormal telangiectasia. There were significant differences in diabetic macular edema, hard exudates grade and fasting blood glucose level between the two groups(P

Diabetic Retinopathy (DR) is a highly consequential condition that significantly impacts individuals with diabetes, often resulting in permanent vision loss if left undetected. The accurate assessment of DR severity heavily relies on the precise segmentation of lesions from fundus images. However, manual segmentation processes are complex and time-consuming. In order to address these limitations, this research introduces a novel approach namely Deep Feature Fused Residual with U-Net (DFFR-U-NET) for the segmentation of DR lesions, specifically Haemorrhage (HM), Hard Exudates (HE), and Optic Disc (OD). The proposed method employs a Convolutional Neural Network (CNN) architecture, which incorporates the U-Net model enhanced with a modified bottleneck using residual blocks. This modified U-Net model, composed of robust convolutional blocks and a modified bottleneck, yields optimal results in the segmentation of DR lesions. The training and validation of the proposed method are conducted using the IDRiD dataset, focusing on HM, HE, and OD segmentation. The experimental results demonstrate that the proposed method achieves a high level of accuracy in the segmentation of HM, HE, and OD lesions. Model performance is assessed using key metrics such as accuracy, precision, Mean Intersection Over Union (IOU), Mean Dice Coefficient, and Average Hausdorff distance. The analysis reveals that the proposed model attains an accuracy of 98% for HM, 99% for HE, and 99% for OD. Precision scores are 98% for HM, 99% for HE, and 99% for OD. Moreover, the Mean IOU values are 0.91 for HM, 0.95 for HE, and 0.99 for OD. The Mean Dice Coefficient for HM is 0.91, for HE is 0.95, and for OD is 0.99. Additionally, the Average Hausdorff distance for the background is 2.84 for HM, 1.24 for HE, and 0.38 for OD, while for lesions, it is 2.84 for HM, 1.24 for HE, and 0.38 for OD. When compared to existing studies, the proposed approach establishes a state-of-the-art performance in retinal lesion segmentation, highlighting its superiority in this domain.

Diabetic macular edema (DME) is a leading cause of vision impairment in diabetic patients, necessitating a timely and accurate diagnosis. This paper proposes a comprehensive system for DME grading using retinal fundus images. Our approach integrates multiple deep learning modules, each designed to address key aspects of the diagnostic process. The first module employs the ConvUNeXt model for segmenting hard exudates (HaEx), crucial indicators of DME. The second module uses RetinaNet for precise optic disc (OD) localization, which is essential for subsequent macula localization. The third module refines macula localization, leveraging preprocessing techniques to enhance image clarity. Finally, our system consolidates these results to provide interpretable DME grading. Experimental evaluations were conducted on the Messidor dataset, demonstrating the system’s robust performance. The HaEx segmentation module achieved a mean IoU of 70.5% and a Dice coefficient of 0.64. The OD localization module showed perfect accuracy, recall, and precision at 1.0. For macula localization, our method satisfied the 1R criterion with 99.38% accuracy. The DME grading module achieved an overall accuracy of 91.12%, with an AUC of 0.9334. Our method offers a balanced performance across accuracy, sensitivity, and specificity compared to other non-interpretable and partially interpretable methods.

Diabetic Retinopathy (DR) is a highly consequential condition that significantly impacts individuals with diabetes, often resulting in permanent vision loss if left undetected. The accurate assessment of DR severity heavily relies on the precise segmentation of lesions from fundus images. However, manual segmentation processes are complex and time-consuming. In order to address these limitations, this research introduces a novel approach namely Deep Feature Fused Residual with U-Net (DFFR-U-NET) for the segmentation of DR lesions, specifically Haemorrhage (HM), Hard Exudates (HE), and Optic Disc (OD). The proposed method employs a Convolutional Neural Network (CNN) architecture, which incorporates the U-Net model enhanced with a modified bottleneck using residual blocks. This modified U-Net model, composed of robust convolutional blocks and a modified bottleneck, yields optimal results in the segmentation of DR lesions. The training and validation of the proposed method are conducted using the IDRiD dataset, focusing on HM, HE, and OD segmentation. The experimental results demonstrate that the proposed method achieves a high level of accuracy in the segmentation of HM, HE, and OD lesions. Model performance is assessed using key metrics such as accuracy, precision, Mean Intersection Over Union (IOU), Mean Dice Coefficient, and Average Hausdorff distance. The analysis reveals that the proposed model attains an accuracy of 98% for HM, 99% for HE, and 99% for OD. Precision scores are 98% for HM, 99% for HE, and 99% for OD. Moreover, the Mean IOU values are 0.91 for HM, 0.95 for HE, and 0.99 for OD. The Mean Dice Coefficient for HM is 0.91, for HE is 0.95, and for OD is 0.99. Additionally, the Average Hausdorff distance for the background is 2.84 for HM, 1.24 for HE, and 0.38 for OD, while for lesions, it is 2.84 for HM, 1.24 for HE, and 0.38 for OD. When compared to existing studies, the proposed approach establishes a state-of-the-art performance in retinal lesion segmentation, highlighting its superiority in this domain. [ABSTRACT FROM AUTHOR]

The leading cause of vision loss globally is diabetic retinopathy. Researchers are making great efforts to automatically detect and diagnose correctly diabetic retinopathy. Diabetic retinopathy includes five stages: no diabetic retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, severe diabetic retinopathy and proliferative diabetic retinopathy. Recent studies have offered several multi-tasking deep learning models to detect and assess the level of diabetic retinopathy. However, the explanation for the assessment of disease severity of these models is limited, and only stops at showing lesions through images. These studies have not explained on what basis the appraisal of disease severity is based. In this article, we present a system for assessing and interpreting the five stages of diabetic retinopathy. The proposed system is built from internal models including a deep learning model that detects lesions and an explanatory model that assesses disease stage. The deep learning model that detects lesions uses the Mask R-CNN deep learning network to specify the location and shape of the lesion and classify the lesion types. This model is a combination of two networks: one used to detect hemorrhagic and exudative lesions, and one used to detect vascular lesions like aneurysm and proliferation. The explanatory model appraises disease severity based on the severity of each type of lesion and the association between types. The severity of the disease will be decided by the model based on the number of lesions, the density and the area of the lesions. The experimental results on real-world datasets show that our proposed method achieves high accuracy of assessing five stages of diabetic retinopathy comparable to existing state-of-the-art methods and is capable of explaining the causes of disease severity. [ABSTRACT FROM AUTHOR]

Abstract Background To explore the characteristics of retina microvascular changes in patients with diabetic nephropathy (DN) and its risk factors. Methods Retrospective, observational study. 145 patients with type 2 diabetic mellitus (DM) and DN were included in the study. Demographic and clinical parameters were obtained from medical records. Presence of diabetic retinopathy (DR), hard exudates (HEs) and diabetic macular edema (DME) were evaluated according to the color fundus images, optical coherence tomography (OCT) and fluorescence angiography (FFA). Results DR accounted for 61.4% in type 2 DM patients with DN, of which proliferative diabetic retinopathy (PDR) accounted for 23.6% and sight threatening DR accounted for 35.7%. DR group had significantly higher levels of low-density lipoprotein cholesterol (LDL-C) (p = 0.004), HbA1c (P = 0.037), Urine albumin creatine ratio (ACR) (p

Background: To explore the characteristics of retina microvascular changes in patients with diabetic nephropathy (DN) and its risk factors. Methods: Retrospective, observational study. 145 patients with type 2 diabetic mellitus (DM) and DN were included in the study. Demographic and clinical parameters were obtained from medical records. Presence of diabetic retinopathy (DR), hard exudates (HEs) and diabetic macular edema (DME) were evaluated according to the color fundus images, optical coherence tomography (OCT) and fluorescence angiography (FFA). Results: DR accounted for 61.4% in type 2 DM patients with DN, of which proliferative diabetic retinopathy (PDR) accounted for 23.6% and sight threatening DR accounted for 35.7%. DR group had significantly higher levels of low-density lipoprotein cholesterol (LDL-C) (p = 0.004), HbA1c (P = 0.037), Urine albumin creatine ratio (ACR) (p < 0.001) and lower level of estimated glomerular filtration rate (eGFR) (P = 0.013). Logistic regression analysis showed DR was significantly associated with ACR stage (p = 0.011). Subjects with ACR stage3 had higher incidence of DR compared with subjects with ACR stage1 (OR = 24.15, 95%CI: 2.06–282.95). 138 eyes of 138 patients were analyzed for HEs and DME, of which 23.2% had HEs in posterior pole and 9.4% had DME. Visual acuity was worse in HEs group than in non-HEs group. There was significant difference in the LDL-C cholesterol level, total cholesterol (CHOL) level and ACR between HEs group and non-HEs group. Conclusions: A relatively higher prevalence of DR was found in type 2 DM patients with DN. ACR stage could be recognized as a risk factor for DR in DN patients. Patients with DN needs ophthalmic examination more timely and more frequently. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a form of diabetic microangiopathy, and vascular hyperpermeability in the macula leads to retinal thickening and concomitant reduction of visual acuity in diabetic macular edema (DME). In this review, we discuss multimodal fundus imaging, comparing the pathogenesis and interventions. Clinicians diagnose DME using two major criteria, clinically significant macular edema by fundus examination and center-involving diabetic macular edema using optical coherence tomography (OCT), to determine the appropriate treatment. In addition to fundus photography, fluorescein angiography (FA) is a classical modality to evaluate morphological and functional changes in retinal capillaries, e.g., microaneurysms, capillary nonperfusion, and fluorescein leakage. Recently, optical coherence tomography angiography (OCTA) has allowed us to evaluate the three-dimensional structure of the retinal vasculature and newly demonstrated that lamellar capillary nonperfusion in the deep layer is associated with retinal edema. The clinical application of OCT has accelerated our understanding of various neuronal damages in DME. Retinal thickness measured by OCT enables us to quantitatively assess therapeutic effects. Sectional OCT images depict the deformation of neural tissues, e.g., cystoid macular edema, serous retinal detachment, and sponge-like retinal swelling. The disorganization of retinal inner layers (DRIL) and foveal photoreceptor damage, biomarkers of neurodegeneration, are associated with visual impairment. Fundus autofluorescence derives from the retinal pigment epithelium (RPE) and its qualitative and quantitative changes suggest that the RPE damage contributes to the neuronal changes in DME. These clinical findings on multimodal imaging help to elucidate the pathology in the neurovascular units and lead to the next generation of clinical and translational research in DME. [ABSTRACT FROM AUTHOR]

Purpose of the study: As per the estimation of the World Health Organization, around 400,000,000 people may be affected by diabetes in the year 2014. It is rapidly increasing double by the upcoming year 2030. Diabetic mellitus, also called diabetes, can cause critical issues of retinal disease such as diabetic retinopathy (DR) which causes blindness. Exudates are considered as the major sign of diabetic maculopathy (DM) which may lead to visual disturbances. Methods: The main purpose is to develop a computer-aided technique for the detection of hard exudates to assist in the diagnosis of DR. It mainly focuses on the identification on the yellow lipids that include hard exudates. The diseased retinal image is given as an input to the classifier; it produces the output as exudates or non-exudates. In the proposed work, the classifier such as support vector machine and multilayer perceptron are used to find out the accurate prediction of exudates and non-exudates. First, the fundus images are allowed to preprocessing technique to get the filtered, contrast enhanced image. Second, the features such as blood vessel segmentation implements morphological operation for the detection of hard exudates by measuring the size of the lesion, and optic disc (OD) is measured and eliminated by comparing the parameters with the size of the lesions. Third, the segmented images are given as an input to the classifier such as SVM and MLP; it classifies and given as an output about the presence or absence of the exudates. In the proposed work, the experimental tests are carried out, and the results are verified on a different set of images. Results: The average accuracy values obtained using SVM and MLP classifiers for 140 images from real-time databases collected from Aravind Eye Hospital, Coimbatore are 88% and 95% respectively. [ABSTRACT FROM AUTHOR]

Abstract Background/aims To evaluate retinal hard exudates (HEs) progression in patients with cystoid macular edema (CME) secondary to diabetic retinopathy (DR) or branch retinal vascular occlusion (BRVO) after intravitreal injections of ranibizumab (IVR) treatment and identify the risk factors for the deterioration of HEs. Methods This retrospective study enrolled 288 eyes with center-involving CME secondary to DR or BRVO from 288 patients (one eye per patient). All patients were treated with three loading doses of ranibizumab intravitreally at a monthly interval. The morphologic features of HEs were observed, and the HEs areas were quantified using a semi-automatic method at baseline, 1 month after the first dose of IVR and 1 month after the third dose of IVR therapy. HEs progression was defined as having a > =2-grade increase in the HEs severity scale. The best-corrected vision acuity (BCVA) and alterations in HEs areas were compared between DR and BRVO groups. And logistic regression analyses were used to identify the risk factors for HEs exacerbation. Results Morphological changes of retinal HEs occurred in all eyes after IVR therapy, although HEs area was not significantly changed in some eyes. DR group has a higher percentage of eyes with progressed HEs area than the BRVO groups (34.9% vs. 21.8%, P = 0.019) 1 month after the first dose of IVR. Both DR and BRVO groups had a decreased percentage of enlarged HEs 1 month after the third injection, but the DR group is still higher than the BRVO group (17.1% vs. 8.4%, P = 0.027). At baseline, there was no correlation between VA and HEs areas. After the first and third doses of IVR, there still was no consistent correlation between HEs severity and change in VA over time. Furthermore, CME with subretinal fluid (SRF) is associated with a higher risk of HEs progression (P = 0.001). Long CME duration and high serum low-density lipoprotein cholesterol (LDL-C) level were identified as risk factors for HEs progression following IVR treatment in both univariable and multivariable regression analyses (Odds ratio (OR) = 1.88, P = 0.012 and OR = 1.14, P = 0.021, respectively). Conclusions Alterations in the area of retinal HEs are widely observed after IVR treatment for CME. The eyes with CME secondary to DR have a higher percentage of progressed HEs than the BRVO eyes. DME with SRF, extended duration of CME, and high LDL-C level are potential risk factors of deteriorated HEs after IVR treatment.

Introduction: This case series aims to present the unusual clinical manifestation of subretinal exudation in patients diagnosed with untreated choroidal melanoma. A total of 886 patients were diagnosed and treated for primary choroidal melanoma between November 2017 and June 2023 at St. Paul's Eye Unit, Royal Liverpool University Hospital, UK. The fundus photographs were screened for lipid exudates by two independent clinical experts. The patients' demographics, clinical manifestations, and imaging features were analysed, whereas the location of exudation was documented with fundus photographs and optical coherence tomography (OCT). The histopathological and genetic results were also analysed in cases with tumour biopsy available., Case Presentations: Eight cases with subretinal exudates were identified ( n = 8/886, 0.90%). No gender predilection was noticed (male/female 1:1), whereas the mean age was 51 years (range 39-79). Four patients were asymptomatic at presentation, 2 patients reported reduced visual acuity, and 2 patients presented with photopsia. OCT scans revealed the presence of subretinal fluid and subretinal exudates, while the ultrasound showed medium or low internal reflectivity in 7 out of 8 cases. The biopsy analysis was available in 4 cases, all showing low-risk spindle cell choroidal melanoma with disomy 3., Conclusion: Lipid exudates are an atypical fundoscopic finding in patients with untreated choroidal melanoma. The subretinal location could differentiate them from other retinal vascular conditions and facilitate early diagnosis and intervention. Interestingly, all cases tested cytogenetically were of low metastatic risk; these exudates may, therefore, be a positive clinical prognostic sign., Competing Interests: The authors have no conflicts of interest to declare., (© 2024 S. Karger AG, Basel.)

Diabetic Retinopathy (DR) is one of diabetes complications that affects our eyes. Hard Exudate (HE) are known to be the early signs of DR that potentially lead to blindness. Detection of DR automatically is a complicated job since the size of HE is very small. Besides, our community nowadays lack awareness on diabetic where they do not know that diabetes can affect eyes and lead to blindness if regular check-up is not performed. Hence, automated detection of HE known as Eye Retinal Imaging System (EyRis) was created to focus on detecting the HE based on fundus image. The purpose of this system development is for early detection of the symptoms based on retina images captured using fundus camera. Through the captured retina image, we can clearly detect the symptoms that lead to DR. In this study, proposed Watershed segmentation method for detecting HE in fundus images. Top-Hat and Bottom-Hat were use as enhancement technique to improve the quality of the image. This method was tested on 15 retinal images from the Universiti Sains Malaysia Hospital (HUSM) at three different stages: Normal, NPDR, and PDR. Ten of these images have abnormalities, while the rest are normal retinal images. The evaluation of the segmentation images would be compared by Sensitivity, F-score and accuracy based on medical expert's hand drawn ground truth. The results achieve accuracy 0.96 percent with 0.99 percent sensitivity for retinal images.

To report a case of late contralateral recurrence of unilateral acute idiopathic maculopathy (UAIM) and its sequelae by using adaptive optics. A 46-year-old woman positive for coxsackie virus presented with a typical UAIM, followed 3 years later by a recurrence in the fellow eye. At an early stage, spectral domain-OCT showed a localized loss of the inner segment/outer segment and cone outer segment tips lines, while flood-illumination adaptive optics displayed pigment clumpings and transient small hard exudates, associated with a persistent blurring of the cone mosaic. These findings support the hypothesis of an outer retinal blood-barrier breakdown, inducing a disruption limited to the outer segment of the photoreceptors, followed by a progressive though incomplete normalization of the cone mosaic characterized by a persistent misalignment of outer segment tips. This would explain the acute clinical presentation of UAIM, followed by a spontaneous, partial, recovery, with a relative scotoma remaining over time. [ABSTRACT FROM AUTHOR]

Purpose: To examine (1) the retinal structure by optical coherence tomography (OCT) and function by means of multifocal electroretinography (mfERG) in eyes with and without nonproliferative diabetic retinopathy (NPDR) (2) for correspondence between local retinal function and OCT zones with retinal lesions. Methods: One hundred and thirty-two eligible participants (30 with nonproliferative DR (NPDR) and 102 with diabetes with no DR) underwent comprehensive ophthalmic examination, optical coherence tomography for retinal thickness measures, mfERG, and ultra-wide field fundus photography. OCT Early Treatment Diabetic Retinopathy Study (ETDRS) grid was overlaid on to mfERG plots. Results: Those with NPDR had significantly thicker full retinal measures in the nine (ETDRS) zones compared to no DR. mfERG P1 latencies in rings 1–6 were significantly delayed, while the response densities in rings 4–6 were lower in the NPDR group. Significant negative correlation was noted between OCT thickness and mfERG P1 response densities in many ETDRS zones. Significant positive correlation was noted between P1 latencies and OCT thickness in a few zones. The combination of cystic spaces, microaneurysms, and hard exudates were present in all zones and were associated with a decrease in P1 response densities compared to no lesions. Reduced P1 response densities were associated with a sporadic delay in the mfERG latencies and vice versa. The number of lesions did not show correspondence to the mfERG measures. Conclusions: In eyes with NPDR, retinal function is differentially correlated with the DR lesions on OCT and can be assessed using multimodal imaging modalities. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) may lead to blindness in diabetic patients, which is one of the most severe eye diseases. Therefore, using automatical technology to detect DR at the early phase has very vital clinical significance. In order to detect the microaneurysms (MAs) and hard exudates (HEs) of DR, a novel detection method based on deep symmetric convolutional neural network is proposed in this paper. The symmetric convolutional structure is used to improve the effectiveness of feature extraction. The proposed method also can overcome the imbalance of positive and negative samples to avoid overfitting by increasing the width and depth of the network. Furthermore, different network structures (convolution, pooling) are used to achieve different feature filtering in the stage of feature extractions. According to the experimental results, the proposed method is superior to the state-of-the-art approach on the public dataset DIARETDB1 (DB1). The detection accuracy of the objects is 92.0%, 93.2%, 93.6%, when using different filtering structures (convolution, max-pooling, ave-pooling) respectively. The detection of microaneurysms is much improved by using ave-pooling layer for feature filtering, and the max-pooling layer can improve the detection of hard exudates.

Background/aims: To evaluate retinal hard exudates (HEs) progression in patients with cystoid macular edema (CME) secondary to diabetic retinopathy (DR) or branch retinal vascular occlusion (BRVO) after intravitreal injections of ranibizumab (IVR) treatment and identify the risk factors for the deterioration of HEs.Methods: This retrospective study enrolled 288 eyes with center-involving CME secondary to DR or BRVO from 288 patients (one eye per patient). All patients were treated with three loading doses of ranibizumab intravitreally at a monthly interval. The morphologic features of HEs were observed, and the HEs areas were quantified using a semi-automatic method at baseline, 1 month after the first dose of IVR and 1 month after the third dose of IVR therapy. HEs progression was defined as having a > =2-grade increase in the HEs severity scale. The best-corrected vision acuity (BCVA) and alterations in HEs areas were compared between DR and BRVO groups. And logistic regression analyses were used to identify the risk factors for HEs exacerbation.Results: Morphological changes of retinal HEs occurred in all eyes after IVR therapy, although HEs area was not significantly changed in some eyes. DR group has a higher percentage of eyes with progressed HEs area than the BRVO groups (34.9% vs. 21.8%, P = 0.019) 1 month after the first dose of IVR. Both DR and BRVO groups had a decreased percentage of enlarged HEs 1 month after the third injection, but the DR group is still higher than the BRVO group (17.1% vs. 8.4%, P = 0.027). At baseline, there was no correlation between VA and HEs areas. After the first and third doses of IVR, there still was no consistent correlation between HEs severity and change in VA over time. Furthermore, CME with subretinal fluid (SRF) is associated with a higher risk of HEs progression (P = 0.001). Long CME duration and high serum low-density lipoprotein cholesterol (LDL-C) level were identified as risk factors for HEs progression following IVR treatment in both univariable and multivariable regression analyses (Odds ratio (OR) = 1.88, P = 0.012 and OR = 1.14, P = 0.021, respectively).Conclusions: Alterations in the area of retinal HEs are widely observed after IVR treatment for CME. The eyes with CME secondary to DR have a higher percentage of progressed HEs than the BRVO eyes. DME with SRF, extended duration of CME, and high LDL-C level are potential risk factors of deteriorated HEs after IVR treatment. [ABSTRACT FROM AUTHOR]

The presence of Hard Exudates in retinal images is one of the significant indications of Diabetic Retinopathy disease. Early detection of Hard Exudates obviously prevents the vision loss very effectively. Today, the development of appropriate Pre-processing stages for Hard Exudate identification through automated screening is an open issue. In this research article, a mathematical morphology based image processing technique is presented for the identification of Hard Exudates in retinal images. The analysis of noisy and low-contrast retinal images shows the importance of this method. In this research work, 570 input images collected from Hospital are examined by the k-fold cross-validation technique. The sensitivity and specificity, two important parameters for performance analysis of Hard Exudate screening, are 97.36% and 99.31%, respectively. In Receiver Operating Characteristics the Area Under Curve (AUC) is 0.93 (average for k folds). Public datasets e_Ophtha_EX and DIARETDB1 are used here to compare the screening performance with existing algorithms and AUC is 0.97 and 0.98, respectively. Thus, the screening result on both Hospital dataset and Public dataset reflects the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Diabetic macular edema (DME) is the most common cause of vision loss in diabetic eyes, and due to the rapid rise in the number of diabetic patients, the treatment burden has increased exponentially. The introduction of antivascular endothelial growth factor (anti-VEGF) therapy has been a major breakthrough in the management of center-involving DME, replacing laser photocoagulation as the first-line treatment. Despite the improvement in DME treatment with anti-VEGF therapy, persistent DME remains a challenge due to the extremely complex pathogenesis and the involvement of several different biochemical pathways. This review focuses on therapeutic options for persistent DME, which include corticosteroids, laser, and surgery. Novel agents for DME control such as new anti-VEGF, interleukin inhibitor, Rho-kinase inhibitor, and neuroprotective agents that are being investigated are reviewed as well. Future treatment perspectives include an individualized DME management. [ABSTRACT FROM AUTHOR]

The tissue layer located at the back of the eye is known as retina which converts the incoming light into nerve signals and those signals are sent to the brain for understanding. The damage onto the retina is termed as retinopathy and that may lead to vision weakening or vision loss. The hard exudates are small white or yellowish white deposits with their edges being clear and sharp. In the proposed methods we take color image of retina then extract the green channel of that image then apply top hat transformation and bottom hat transformation on that image. The DIARETDB1 and High-Resolution Fundus (HRF) databases are used for performance evaluation of the proposed method. The proposed technique achieves accuracy 97%, sensitivity 95%, and specificity 96% and it takes average 5.6135 second for detection of hard exudates in an image.

Objectives: To determine the efficacy and safety of brolucizumab therapy administered on a pro re nata (PRN) basis without loading dose in treatment naïve patients with diabetic macular edema (DME) for 1 year follow-up., Methods: Patients with recent DME (<6 months) received a mandatory brolucizumab injection at inclusion and other injections could be given on a PRN basis with an 8-week interval (between injections) at minimum. Rescue therapy with other anti-VEGF was possible in case of incomplete DME resolution after the second brolucizumab with a minimum of 1-month treatment free interval between 2 injections. The primary outcome measure was the change in (BCVA) at 12 months. Secondary outcome measures included the change in central subfield thickness (CST), the change in hard exudate surface area and microaneurysms at 1 year., Results: A total of 53 patients were included. At 12 months, the mean (SD) number of injections was 2.6 (0.8) in addition to the first mandatory injection. The mean (SD) interval between 2 consecutive injections was 3.2 (1.4) months. The mean (SD) BCVA improved from 0.62 (0.1) logMAR to 0.40 (0.16) logMAR (p = 0.012). The mean CST reduced from 397.0 (47.2) µm to 224.5 (28.1) µm (p = 0.013). The hard exudate surface area decreased significantly (p = 0.012) as did microaneurysms (p = 0.02). Seven patients required at least 1 rescue therapy. No patients experienced intra-ocular inflammatory adverse events., Conclusion: Brolucizumab therapy for DME is a safe and effective modality for the treatment of recent DME and has the potential to reduce the number of injections.

Aims: To quantify hard exudates (HEs) by multicolor imaging (MCI) and traditional color fundus photography (CFP) in diabetic macular edema (DME), and study their associations with serum lipid levels. Methods: Observational study. DME patients with HEs were recruited. The HE area and location both by MCI and CFP were measured by ImageJ software. Multivariate regression models were used to analyze the associations of serum lipid levels with the total HE area and HE location. Results: Sixty-two patients (74 eyes) were enrolled to quantify HEs in DME. The total HE area by MCI was larger than that by CFP (P = 0.004), and the distance between the fovea and the nearest HE by MCI was shorter than that by CFP (P = 0.003). The percentage of patients with HEs involving the central macula by MCI was significantly higher than that by CFP (P < 0.001). Furthermore, 62 eyes of 62 patients were included to analyze the associations of HE parameters with serum lipid levels. In both MCI and CFP, the HE areas were positively associated with triglyceride level (P = 0.016, P = 0.022, respectively). HEs involving the central macula were positively associated with triglyceride and low-density cholesterol levels in MCI (P = 0.028, P = 0.046, respectively), while no significant association was found between serum lipid levels and HE location in CFP. Conclusions: MCI is superior to traditional CFP for the detection of HEs and the analysis of associations between HEs and serum lipid levels in DME. [ABSTRACT FROM AUTHOR]

Diabetic macular edema (DME) is the main cause of visual impairment in diabetic patients. Early detection of DME will significantly reduce the risk of vision loss for the patients. According to the clinical DME grading standard, the positional relationship between Hard Exudates (HEs) and macular center is an important basis for DME grading. Accurate DME grading is thus predicated on properly locating the macular center and segmenting HEs. HEI-MED and E-ophtha EX data sets were tested by the proposed DME grading method, reaching an average accuracy of 94.4% and 87%, respectively. The proposed method was also tested by comparison against other commonly used methods as per its potential to assist doctors in initially screening DME; it was found to not only improve the efficiency of DME detection, but also to save Optical Coherence Tomography medical resources over the other methods tested. [ABSTRACT FROM AUTHOR]

The presence of hard exudate (EX) lesions is an early clinical symptom of Diabetic Retinopathy (DR); its accurate segmentation is essential for diagnosis and treatment. Automatic segmentation of EX lesions is challenging because they have a wide range of sizes, shapes, and brightness, and can be confused with soft exudate lesions. In this paper, we present and assess an efficient segmentation method for interactively segmenting EX lesions in fundus images from DR patients. Our approach consisted of two main stages: (1) the first stage generates an automatic segmentation using a CNN-based method. (2) If the obtained segmentation is suboptimal, the second stage is used to refine each inaccurately segmented region using a CNN-based interactive segmentation method. To train and evaluate method performance, we used two public datasets (IDRiD and DDR) and two local datasets from two medical centers (EHos and EWT) containing a wide range of EX lesions. Several state-of-the art CNN-based models and the proposed method were trained and evaluated on IDRiD, DDR and EHos datasets using the area under the precision–recall curve (AUPR), the dice similarity coefficient (Dice) and the intersection over union (IoU). In addition, EWT dataset was used to evaluate the methods without retraining the CNN models. The evaluation results using IDRiD dataset showed that the proposed method achieved AUPR , mean Dice and mean IoU scores of 0.893, 76.6% and 62.4%, respectively, which are comparable to the state-of-the-art method on EX segmentation. When evaluated on the DDR dataset with large portion of small EX lesions, the proposed method achieved AUPR , mean Dice and mean IoU scores of 0.693, 60.8% and 45.5%, yielding state-of-the-art performance. Furthermore, with the main evaluation metric being AUPR , the evaluation results on two datasets, EHos and ETW, with scores of 0.689 and 0.667 demonstrated superiority of the proposed method over the other segmentation methods. The proposed two-stage CNN-based segmentation method is an useful approach for accurately segmenting EX lesions in color fundus images, enabling lesion quantification to aid experts in treating patients with DR. The source code, the trained CNN models and the data are publicly available at: https://github.com/dvquang2000/Interactive_HardExudates_Segmentation. • A two-stage CNN-based method was proposed for accurate EX lesions segmentation in fundus image • Multiple datasets in several hospitals and countries were included in the study • An assessment of the proposed method on two different field of view in image acquisition was performed • State-of-the-art and the proposed methods were tested with and without retraining the CNN models • The proposed method outperformed state-of-the-art methods in most evaluation scores [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy is a recurrent retinal disorder that can affect patients with both type 1 and type 2 diabetes. From a large amount of data available, it is apparent that Diabetic Retinopathy is a progressive disease that can terminate in severe vision problems or complete vision loss. The clinical symptoms of Diabetic Retinopathy include microaneurysms, haemorrhages, hard and soft exudates. They are lesions seen on the surface of the retina among diabetic patients. They indicate a pre-proliferative Diabetic Retinopathy state and needs to be treated. This paper describes a method based on morphological operations and thresholding for detection of clinical symptoms of Diabetic Retinopathy and grading them. As a part of the feature extraction stage, feature level fusion comprising LBP and HOG features is explored as it yields better accuracy in detection of affected areas in fundus images. Various classifiers namely; SVM, k-NN, Decision tree and ECOC classifiers have been tested. The average accuracy values for detection of microaneurysms and hemorrhages using fusion features yielded 95% for SVM, 97% for k-NN and 96% for Decision tree classifier respectively and the average accuracy values for detection of hard and soft exudates using fusion features yielded is 98% for SVM, 91% for k-NN and 98% for Decision tree classifier respectively. The proposed method using fusion features for grading of Diabetic Retinopathy yielded an accuracy of 32% for k-NN, 91% for Decision tree and 98% for ECOC classifier respectively. The methods and algorithms developed as a part of this research work will aid the ophthalmologists and clinicians in early detection of retinal disorders. It will also be useful for mass screening programs particularly in rural areas and developing nations. [ABSTRACT FROM AUTHOR]

Jay Chhablani, Mahima Jhingan, Abhilash Goud, Kiran Kumar Vupparaboina, Taraprasad Das Smt Kanuri Santhamma Retina Vitreous Centre, LV Prasad Eye Institute, Hyderabad, Telangana, India Purpose: This study aimed to evaluate the effect of dexamethasone implantation on the hard exudates (HEX) in patients with diabetic macular edema (DME). Study design: This was a nonrandomized open-label single-center prospective trial. Methods: This study included 15 eyes of 11 subjects with DME. Key inclusion criteria were naïve eyes with DME with HEX within 3 mm of fovea with center-involving DME; central macular thickness (CMT) >250 µm at baseline; best-corrected visual acuity (BCVA) between 20/400 and 20/40. Key exclusion criteria were previous intraocular surgery and history of panretinal photocoagulation (PRP) in past 4 months. The primary outcome measure was change in total HEX area at the macula (in mm2) measured by semiautomated algorithm. Secondary outcome measures were change in visual acuity, low-contrast visual acuity (LCVA), retinal sensitivity (RS) on macular microperimetry, and CMT. Results: The total HEX area reduced from 1.5 mm2 (±1.46 mm2) at baseline to 0.89 mm2 (±1.062 mm2) at the final visit (p=0.185). The CMT improved significantly (p=0.03) from 488.67 µm (±240.66 µm) to 326.93 µm (±135.84 µm) at the final visit. Mean BCVA remained stable (p=0.95) (50.93±16.65 at baseline and 50.6±18.95 at final visit). The mean LCVA and RS showed insignificant improvement (p=0.31 and p=0.28, respectively). Conclusion: Our pilot study demonstrated an improving trend in reduction of total HEX area and other anatomical outcomes, with limited functional outcomes. Larger randomized studies with a larger sample size with a control group are warranted to establish management protocols for DME with significant subfoveal HEX. Keywords: dexamethasone implant, Ozurdex, hard exudates, diabetic macular edema

AIM: To clarify this controversy and to provide evidence for application of lipid lowering agents in treatment of diabetic retinopathy (DR). METHODS: We searched the databases of PubMed, Embase and Cochrane Library Central Register of Controlled Trials (CENTRAL) and abstracts from main annual meetings up to January 1, 2017. Google scholar and ClinicalTrials.gov were also searched for unpublished relevant studies. We included randomized controlled trials (RCTs) that studied lipid-lowering agents in type 1 or type 2 diabetes in this Meta-analysis. The primary endpoint was the progression of DR, and the secondary endpoints included vision loss, development of diabetic macular edema (DME) and aggravation of hard exudates. The pooled odds ratios (OR) with corresponding 95% confidence intervals (95%CIs) were calculated. RESULTS: After systemic and manual literature search by two independent investigators, we included 8 RCTs from 7 published articles with 13 454 participants in this Meta-analysis. The results revealed that lipid-lowering drugs were associated with reduced risk in DR progression [OR=0.77 (95%CI: 0.62, 0.96), P=0.02]. Lipid-lowering agents might have protective effect on DME compared to placebo, although the difference was not statistically significant [OR=0.60 (95%CI: 0.34, 1.08), P=0.09]. However, no significant differences in the worsening of vision acuity [OR=0.96 (95%CI: 0.81,1.14), P=0.64] and hard exudates [OR=0.50 (95%CI:0.15, 1.74), P=0.28] were found between the lipid-lowering drugs and the placebo groups. CONCLUSION: In DR patients, lipid-lowering agents show a protective effect on DR progression and might be associated with reduced risk in the development of DME. However, lipid-lowering agents have no effects on vision loss and hard exudates aggravation. Further clinical trials in larger scale are required to confirm the conclusion of this study and thus justify the use of intensive control lipids with anti-lipid agents at the early stages of DR.

Based on the luminance and edge characteristics of the exudates in fundus images, a multi-algorithm fusion method for automatic detection of the exudate is adopted in this paper to solve the problems of low sensitivity of the traditional algorithm and interference of dark lesions such as optic disc and other microangiomas in the detection results. In order to improve the segmentation efficiency and accuracy, this study uses top-hat and boottom-hat to enhance the image contrast of the original image, and then a dual threshold segmentation method combining genetic algorithm and optimal histogram entropy method is proposed to preliminarily segment the image. The experimental results show that the sensitivity and PPV of the algorithm are 83.6% and 93.2% at the pixel level, and the SE, specificity and accuracy are 95.2%, 86.2% and 90.8% respectively at the image level. The results obtained by testing on another independent DIARETDB1 database are 82.4%, 93.3%, 93.6%, 96.2%, 89.9%. Compared with other algorithms, this method can distinguish the exudates from other dark lesions, and the detection time is short, accurate and efficient. [ABSTRACT FROM AUTHOR]

The patients with diabetes have a chance to develop diabetic retinopathy (DR) which affects to the eyes. DR can cause blindness if the patients do not control diabetes. The patients with DR will have an impairment of metabolism of glucose causing a high glucose level in blood vessel called hyperglycemia. It leads to abnormal blood vessel and ultimately results in leakage of blood or fluid like lipoproteins, which are deposited under macular edema called hard exudates. They are normally white or yellowish-white with margins. Hard exudates are often arranged in clumps or circinate rings and located in the outer layer of the retina. The aim of this research was to detect hard exudates by applying several image processing techniques and classify them by using supervised learning methods including support vector machines and some neural network approaches, i.e., multilayer perceptron (MLP) network, hierarchical adaptive neurofuzzy inference system (hierarchical ANFIS), and convolutional neuron networks. DIARETDB1 which contains 89 fundus images is exploited as a dataset for evaluation. Hard exudate candidates are extracted by morphological techniques and classified by the classifiers trained by extracted patches with the corresponding ground truths. The tenfold cross-validation is applied to assure the generalization of the results. The proposed method achieves the area under the curve (AUC) of 0.998 when the MLP network is applied. The AUCs for all four classifiers are more than 0.95. This shows that the combination of image processing techniques and suitable classifiers can perform very well in hard exudate detection problem. [ABSTRACT FROM AUTHOR]

Segmentation of lesions from fundus images is an essential prerequisite for accurate severity assessment of diabetic retinopathy. Due to variation in morphologies, number and size of lesions, the manual grading process becomes extremely challenging and time-consuming. This necessitates the need of an automatic segmentation system that can precisely define the region of interest boundaries and assist ophthalmologists in speedy diagnosis along with diabetic retinopathy severity grading. The paper presents a modified U-Net architecture based on residual network and employs periodic shuffling with sub-pixel convolution initialized to convolution nearest neighbour resize. The proposed architecture has been trained and validated for microaneurysm and hard exudate segmentation on two publicly available datasets namely IDRiD and e-ophtha. For IDRiD dataset, the network obtains 99.88% accuracy, 99.85% sensitivity, 99.95% specificity and dice score of 0.9998 for both microaneurysm and exudate segmentation. Further, when trained on e-ophtha and validated on IDRiD dataset, the network shows 99.98% accuracy, 99.88% sensitivity, 99.89% specificity and dice score of 0.9998 for microaneurysm segmentation. For exudates segmentation, the model obtained 99.98% accuracy, 99.88% sensitivity, 99.89% specificity and dice score of 0.9999, when trained on e-ophtha and validated on IDRiD dataset. In comparison to existing literature, the proposed model provides state-of-the-art results for retinal lesion segmentation. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a chronic disease of the retinal microvasculature which leads to loss of central visual acuity. Early detection of hard exudates in retinal images using computer aided tool helps the ophthalmologist to diagnose the blindness problem. This article presents a novel method to detect and classify the hard exudates in retinal images. For detection, the optic disc (OD) of the retinal image is masked and then the bright patches that contribute to hard exudates are segmented based on thresholding and morphological reconstruction techniques. Here OD is identified using brightness and variance features of the OD followed by Circular Hough Transformation. For classification, features such as color, size, and texture are extracted from each segmented candidate regions based on these features and the regions are classified by using multilayered perceptron neural network (MLP). The proposed method is experimented on the DIARETDB1 retinal dataset and also compared with the existing methods. [ABSTRACT FROM AUTHOR]

Diabetic maculopathy is one of the leading complications of diabetes that is measured as the significant reason for vision misfortune among individuals around the globe. In this paper, a two stage approach is proposed for automatic detection and grading of diabetic maculopathy based on mathematical morphology. Mainly two features are needed for the detection of maculopathy. One is the macula and other one is hard exudates, based on macula location and Optic Disc (OD) diameter, macular section has been drawn. We validate the presence or absence of hard exudates in the macula section and severity of the maculopathy was assessed. The experiment results performed on images from public database i.e., MESSIDOR indicate that the proposed method achieved encouraging results. [ABSTRACT FROM AUTHOR]