Color Retinal Images Research Articles

Revolutionizing diabetic retinopathy detection using DB-SCA-UNet with Drop Block-Based Attention Model in deep learning for precise analysis of color retinal images

Revolutionizing diabetic retinopathy detection using DB-SCA-UNet with Drop Block-Based Attention Model in deep learning for precise analysis of color retinal images

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

Effect of Low-Dose Aspirin on the Course of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in old age. There is no proven intervention to prevent AMD and, apart from lifestyle, nutritional, and supplement advice, there is no intervention to delay its progression. To determine the impact of long-term low-dose aspirin on the incidence and progression of AMD. The Aspirin in Reducing Events in the Elderly-AMD (ASPREE-AMD) study was an Australian-based substudy of the ASPREE trial, a multicenter, international, randomized, double-masked, placebo-clinical trial investigating the efficacy of low-dose aspirin in prolonging disability-free survival among older individuals. Retinal photography was conducted at baseline from March 2010 to January 2015, then 3 and 5 years after randomization. AMD status was determined using color retinal images and treatment records. Australian participants in ASPREE aged 70 years and older without dementia, independence-limiting physical disability, cardiovascular disease, or chronic illness limiting 5-year survival and with gradable retinal images at baseline were included. Data were analyzed from December 2022 to December 2023. Aspirin (100 mg daily, enteric coated) or placebo. Incidence of AMD and progression from early/intermediate to late AMD. Outcomes were analyzed by modified intention-to-treat analysis. A total of 4993 participants were enrolled in this substudy. Baseline characteristics were similar between groups. At the time of sponsor-determined trial termination, retinal follow-up data were available for 3208 participants, 3171 of whom were analyzed for AMD incidence and progression, with a median (IQR) age of 73.5 (71.5-76.4) years and even sex distribution (1619 [51%] female). Median (IQR) follow-up time was 3.1 (3.0-3.5) years. Cumulative AMD incidence was 195 of 1004 (19.4%) in the aspirin group and 187 of 979 (19.1%) in the placebo group (relative risk [RR], 1.02; 95% CI, 0.85-1.22; P = .86). Cumulative progression from early/intermediate AMD to late AMD was observed in 14 of 615 (2.3%) participants in the aspirin group and 18 of 573 (3.1%) in the placebo group (RR, 0.72; 95% CI, 0.36-1.44; P = .36). In this trial, low-dose aspirin administered for 3 years did not affect the incidence of AMD. The evidence was weaker for progression of AMD due to low number of progressed cases. Overall, these results do not support suggestion that low-dose daily aspirin prevents the development or progression of AMD. anzctr.org Identifier: ACTRN12613000755730.

On Performance Analysis Of Diabetic Retinopathy Classification

This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-order PDE is used. Using OCT retinal fundus images, the most effective feature extraction method is identified.

A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images

A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images

Explainable artificial intelligence model for the detection of geographic atrophy using colour retinal photographs

ObjectiveTo develop and validate an explainable artificial intelligence (AI) model for detecting geographic atrophy (GA) via colour retinal photographs.Methods and analysisWe conducted a prospective study where colour fundus images were...

Extraction of Retinal Blood Vessels in Colour Retinal Images: An Analysis

Extraction of Retinal Blood Vessels in Colour Retinal Images: An Analysis

Multi-level spatial-temporal and attentional information deep fusion network for retinal vessel segmentation

Objective. Automatic segmentation of fundus vessels has the potential to enhance the judgment ability of intelligent disease diagnosis systems. Even though various methods have been proposed, it is still a demanding task to accurately segment the fundus vessels. The purpose of our study is to develop a robust and effective method to segment the vessels in human color retinal fundus images. Approach. We present a novel multi-level spatial-temporal and attentional information deep fusion network for the segmentation of retinal vessels, called MSAFNet, which enhances segmentation performance and robustness. Our method utilizes the multi-level spatial-temporal encoding module to obtain spatial-temporal information and the Self-Attention module to capture feature correlations in different levels of our network. Based on the encoder and decoder structure, we combine these features to get the final segmentation results. Main results. Through abundant experiments on four public datasets, our method achieves preferable performance compared with other SOTA retinal vessel segmentation methods. Our Accuracy and Area Under Curve achieve the highest scores of 96.96%, 96.57%, 96.48% and 98.78%, 98.54%, 98.27% on DRIVE, CHASE_DB1, and HRF datasets. Our Specificity achieves the highest score of 98.58% and 99.08% on DRIVE and STARE datasets. Significance. The experimental results demonstrate that our method has strong learning and representation capabilities and can accurately detect retinal blood vessels, thereby serving as a potential tool for assisting in diagnosis.

Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy.

Eye diseases such as diabetic retinopathy are progressive with various changes in the retinal vessels, and it is difficult to analyze the disease for future treatment. There are many computerized algorithms implemented for retinal vessel segmentation, but the tiny vessels drop off, impacting the performance of the overall algorithms. This research work contains the new image processing techniques such as enhancement filters, coherence filters and binary thresholding techniques to handle the different color retinal fundus image problems to achieve a vessel image that is well-segmented, and the proposed algorithm has improved performance over existing work. Our developed technique incorporates morphological techniques to address the center light reflex issue. Additionally, to effectively resolve the problem of insufficient and varying contrast, our developed technique employs homomorphic methods and Wiener filtering. Coherent filters are used to address the coherence issue of the retina vessels, and then a double thresholding technique is applied with image reconstruction to achieve a correctly segmented vessel image. The results of our developed technique were evaluated using the STARE and DRIVE datasets and it achieves an accuracy of about 0.96 and a sensitivity of 0.81. The performance obtained from our proposed method proved the capability of the method which can be used by ophthalmology experts to diagnose ocular abnormalities and recommended for further treatment.

Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images

Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient’s condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.

Diagnosis of Diabetic Retinopathy using Fundus Images and Image Processing Methods

Abstract: Diabetes is a metabolic condition that causes organ failure, including retinal damage. Diabetic Retinopathy causes blindness by leaking blood vessel fluid into the retina also called as exudates. Thus, there has been a surge of interest in automated methods for detecting diabetic retinopathy using image processing techniques. Using real-time image dataset and image processing techniques, the proposed system is able to detect major retinal abnormalities such as enlarged retinal veins, blood spots, and white-yellow patches that indicate fluid leakage. The system analyses colour retinal images to determine the extent of damage and locate clusters of injured pixels within the region. The technique has a greater detection accuracy of 88%, and it has been demonstrated to be successful in detecting exudates and abnormalities from fundus images. In addition, the system is extremely helpful. To make suggestions for possible treatments based on an examination of the fundus images

Polypoidal Choroidal Vasculopathy: Updates on Risk Factors, Diagnosis, and Treatments.

There have been recent advances in basic research and clinical studies in polypoidal choroidal vasculopathy (PCV). A recent, large-scale, population-based study found systemic factors, such as male gender and smoking, were associated with PCV, and a recent systematic review reported plasma C-reactive protein, a systemic biomarker, was associated with PCV. Growing evidence points to an association between pachydrusen, recently proposed extracellular deposits associated with the thick choroid, and the risk of development of PCV. Many recent studies on diagnosis of PCV have focused on applying criteria from noninvasive multimodal retinal imaging without requirement of indocyanine green angiography. There have been attempts to develop deep learning models, a recent subset of artificial intelligence, for detecting PCV from different types of retinal imaging modality. Some of these deep learning models were found to have high performance when they were trained and tested on color retinal images with corresponding images from optical coherence tomography. The treatment of PCV is either a combination therapy using verteporfin photodynamic therapy and anti-vascular endothelial growth factor (VEGF), or anti-VEGF monotherapy, often used with a treat-and-extend regimen. New anti-VEGF agents may provide more durable treatment with similar efficacy, compared with existing anti-VEGF agents. It is not known if they can induce greater closure of polypoidal lesions, in which case, combination therapy may still be a mainstay. Recent evidence supports long-term follow-up of patients with PCV after treatment for early detection of recurrence, particularly in patients with incomplete closure of polypoidal lesions.

A deep neural network and machine learning approach for retinal fundus image classification

Diabetes is a common chronic disease and a major public health problem approaching epidemic proportions globally. People with diabetes are more likely to suffer from glaucoma than people without diabetes. Glaucoma can lead to loss of vision if not diagnosed at an early stage. This study proposes an intelligent computer-aided triage system with a deep neural network and machine learning to develop and analyze color retinal fundus images and classify glaucomatous retinal images. Deep features of retinal images from the fundus retinal image are extracted using a deep neural network, and the classification of features is performed and analyzed using different machine learning classifiers. Experimental results show that the combination of deep neural network and logistic regression-based classifier outperforms all existing glaucomatous triage systems, improving classification accuracy, sensitivity, and specificity.

IMPLEMENTATION OF CLAHE CONTRAST ENHANCEMENT & OTSU THRESHOLDING IN RETINAL IMAGE PROCESSING

In both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automatic or computer-aided diagnosis systems. Unlike typical foreground and background segmentation in normal image processing, there are three problems making the retinal vessel segmentation task even harder. First, the retinal color image tends to be red everywhere, thus having a lower contrast than normal image segmentation. Second, most retinal color images suffer from unbalanced illumination and make it harder to recognize background. Third, the symptom for retinopathy has unexpected color and shape, thus making it more difficult to separate vessel from noises. Under such circumstance, the research of the retinal blood vessel segmentation has brought much attention and been developed. In this work, the input is a retinal color image and the output will be a binary image of the vessel and non-vessel pixels. Preprocessing with CLAHE contrast enhancement, Vessel Extraction with Otsu thresholding is implemented along with performance parameters Accuracy, Sensitivity and Specificity. In the proposed work 20 images are used from the database called Digital Retinal Images for Vessel Extraction. The performance parameters Specificity, Sensitivity, Accuracy are evaluated.

Use of Segment Anything Model (SAM) and MedSAM in the optic disc Segmentation of colour retinal fundus images: Experimental Finding

Detection of Optic Disc segmentation in retinal fundus images is important step in identification of various abnormal conditions like diabetic retinopathy, Glaucoma etc. and is an important part of eye that is routinely examined. We have used Segment Anything model (SAM) by Meta AI and based fine-tuned model MedSAM for segmentation of optic disc in retinal fundus images. We have used Indian Diabetic Retinopathy dataset (IDRiD) segmentation part. It consists of 81 original colour fundus images in jpg files split into train and test set. Ground-truth images for the lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates divided into train and test set - TIF Files) and Optic Disc (divided into train and test set - TIF Files). This dataset is feed into the deep learning neural network model; Segment Anything Model (SAM) and MedSAM for the image segmentation task. The Dice Similarity Coefficient (DSC) is used in the experiment for observing the model performance. We have used 80% for training and 20% for testing of Indian Diabetic Retinopathy Dataset (IDRiD) in both models. After 100 epochs with 32 batch size it is observed that average Dice Similarity Coefficient (DSC) of SAM and MedSAM models in the optic disc segmentation task are, 85.97 % and 90.15 %. Also, in finding it is observed that SAM and MedSAM both have very low DSC where the fundus images are having very high brightness.

Use of Segment Anything Model (SAM) and MedSAM in the optic disc Segmentation of colour retinal fundus images: Experimental Finding

Detection of Optic Disc segmentation in retinal fundus images is important step in identification of various abnormal conditions like diabetic retinopathy, Glaucoma etc. and is an important part of eye that is routinely examined. We have used Segment Anything model (SAM) by Meta AI and based fine-tuned model MedSAM for segmentation of optic disc in retinal fundus images. We have used Indian Diabetic Retinopathy dataset (IDRiD) segmentation part. It consists of 81 original colour fundus images in jpg files split into train and test set. Ground-truth images for the lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates divided into train and test set - TIF Files) and Optic Disc (divided into train and test set - TIF Files). This dataset is feed into the deep learning neural network model; Segment Anything Model (SAM) and MedSAM for the image segmentation task. The Dice Similarity Coefficient (DSC) is used in the experiment for observing the model performance. We have used 80% for training and 20% for testing of Indian Diabetic Retinopathy Dataset (IDRiD) in both models. After 100 epochs with 32 batch size it is observed that average Dice Similarity Coefficient (DSC) of SAM and MedSAM models in the optic disc segmentation task are, 85.97 % and 90.15 %. Also, in finding it is observed that SAM and MedSAM both have very low DSC where the fundus images are having very high brightness.

Use of Segment Anything Model (SAM) and MedSAM in the optic disc Segmentation of colour retinal fundus images: Experimental Finding

Detection of Optic Disc segmentation in retinal fundus images is important step in identification of various abnormal conditions like diabetic retinopathy, Glaucoma etc. and is an important part of eye that is routinely examined. We have used Segment Anything model (SAM) by Meta AI and based fine-tuned model MedSAM for segmentation of optic disc in retinal fundus images. We have used Indian Diabetic Retinopathy dataset (IDRiD) segmentation part. It consists of 81 original colour fundus images in jpg files split into train and test set. Ground-truth images for the lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates divided into train and test set - TIF Files) and Optic Disc (divided into train and test set - TIF Files). This dataset is feed into the deep learning neural network model; Segment Anything Model (SAM) and MedSAM for the image segmentation task. The Dice Similarity Coefficient (DSC) is used in the experiment for observing the model performance. We have used 80% for training and 20% for testing of Indian Diabetic Retinopathy Dataset (IDRiD) in both models. After 100 epochs with 32 batch size it is observed that average Dice Similarity Coefficient (DSC) of SAM and MedSAM models in the optic disc segmentation task are, 85.97 % and 90.15 %. Also, in finding it is observed that SAM and MedSAM both have very low DSC where the fundus images are having very high brightness.

Enhancement method with naturalness preservation and artifact suppression based on an improved Retinex variational model for color retinal images.

Retinal images are widely used for the diagnosis of various diseases. However, low-quality retinal images with uneven illumination, low contrast, or blurring may seriously interfere with diagnosis by ophthalmologists. This study proposes an enhancement method for low-quality retinal color images. In this paper, an improved variational Retinex model for color retinal images is first proposed and applied to each channel of the RGB color space to obtain the illuminance and reflectance layers. Subsequently, the Naka-Rushton equation is introduced to correct the illumination layer, and an enhancement operator is constructed to improve the clarity of the reflectance layer. Finally, the corrected illuminance and enhanced reflectance are recombined. Contrast-limited adaptive histogram equalization is introduced to further improve the clarity and contrast. To demonstrate the effectiveness of the proposed method, this method is tested on 527 images from four publicly available datasets and 40 local clinical images from Tianjin Eye Hospital (China). Experimental results show that the proposed method outperforms the other four enhancement methods and has obvious advantages in naturalness preservation and artifact suppression.

A two-stage histogram equalization enhancement scheme for feature preservation in retinal fundus images

Early detection of ophthalmologic complications in the retinal fundus images is crucial and requires a quality image for accurate analysis and diagnosis. This often proves tricky due to the visual complexities of some retinal fundus images resulting in over-enhancement, non-uniform contrast, artificial boundaries, abrupt colour change, and other undesirable limitations in the performance of the existing image enhancement methods. In this paper, the uneven illumination and colour balance are globally adjusted using a histogram match. At the same time, the luminance component (V) of the HSV colour space is equalized with CLAHE for local contrast enhancement. Eight control parameter values are investigated to choose the most suitable values to restrict excessive enhancement and control the quality of the enhanced image. The performance of the proposed enhancement algorithm on the STARE, DRIVE, HRF, and DIARETDB1 databases shows that the method can prominently enhance colour retinal fundus images. The visual and quantitative comparisons of the proposed method with state-of-the-art techniques are remarkably outstanding. The statistical analysis test achieves a ρ-value far<0.05 justifying the statistical significance of the quantitative metrics and the high-quality performance of our enhancement framework.

GÖRÜNTÜ FİLTRELEME İLE DENETİMSİZ RETİNA DAMAR BÖLÜTLENMESİ İÇİN PARAMETRE ENİYİLEŞTİRİLMESİ

Göz hastalıklarının tespiti ve değerlendirilmesi için retina görüntüleri fundus adı verilen özelleştirilmiş bir kamera sistemi ile sayısal ortamda elde edilmektedir. Çeşitli gürültüler ve keskin olmayan zıtlık dolayısıyla gözdeki damarların uzmanlar tarafından tespiti zorlaşmakta ve bu durum uzmanların teşhis koymasını zorlaştırabilmektedir. Bu çalışmada, fundus görüntülerinden retina damar örgüsü bölütlenme başarısını arttırmak amacıyla denetimsiz görüntü işleme tabanlı matematiksel morfoloji ve Coye filtreleme ve bağlantılı bileşen analizi yaklaşımları kullanılmıştır. Ek olarak, retina görüntüleri gürültü giderme ve zıtlık arttırmak için ön işlemden geçirilmiştir. Denetimsiz görüntü işleme tabanlı yaklaşımların başarısını arttırmak üzere parametre optimizasyonu yapılmıştır. Görüntü işlemede sıklıkla kullanılan kontrast sınırlı adaptif histogram eşitleme (KSAHE) yönteminde renkli retina görüntüleri için en uygun kontrast üst sınır değeri araştırılmıştır. Önerilen yaklaşım, araştırmacıların erişimine açık DRIVE ve STARE veri kümelerinde test edilmiştir. Önceki denetimsiz öğrenme çalışmalarına kıyasla bazı metriklerde başabaş ve bazı metriklerde daha başarılı sonuçlara ulaşılmıştır.