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Early identification is essential to prevent blindness in glaucoma patients since the disease is an optic neuropathy that, if left untreated, leads to irreversible vision loss. Because it makes it possible to analyze important structural features such as the Optic Cup and Disc (OC and OD), whose morphological alterations are markers of disease progression, retinal fundus imaging is essential for glaucoma diagnosis. Deep learning breakthroughs have led to improvements in segmentation approaches, which help early diagnosis and therapy by examining retinal structures with greater accuracy and efficiency. Numerous methods for segmenting retinal fundus images are reviewed in this paper, including those for Optic Nerve Head (ONH), retinal vasculature, OD, OC, and macula segmentation. In addition to cutting-edge deep learning designs like U-Net, Mask R-CNN, Fully Convolutional Networks (FCN), and DeepLab, conventional techniques like thresholding and region growth are investigated. By improving segmentation precision with machine and deep learning algorithms, less manual interpretation by human experts is required. A comparison of various methods is given, with consideration to datasets, performance measures, advantages, and disadvantages. While Graph Convolutional Networks (GCNs) and Convolutional Neural Networks (CNNs) perform well in tasks like glaucoma detection and classification, more notable approaches include UNet and Mask RCNN, which have proven to have enormous potential in OD/OC segmentation. In addition, the paper discusses the obstacles that glaucoma detection currently faces, such as inconsistent images, limited datasets, and the requirement for more broadly applicable models. To advance automated glaucoma detection and enhance patient care, several challenges must be overcome.

Glaucoma is an eye disease that causes loss of vision and blindness by damaging a nerve in the back of the eye called optic nerve. The optic nerve collects the visual information from the eyes and transmits to the brain. Glaucoma is mainly caused by an abnormal high pressure in the eyes. Over time, the increased pressure can erode the tissues of optic nerve, leading to vision loss or blindness. If it is diagnosed in advance, then only it can prevent the vision loss. To diagnose the glaucoma, it must accurately differentiate between the optic disc (OD), optic cup (OC), and the retinal nerve fiber layer (RNFL). The segmentation of the OD, OC, and RNFL remains a challenging issue under a minimum contrast image of boundaries. Therefore, in this study, an innovative method of Hybrid Symbiotic Differential Evolution Moth-Flame Optimization (SDMFO)-Multi-Boost Ensemble and Support Vector Machine (MBSVM)-based segmentation and classification framework is proposed for accurately detecting the glaucoma disease. By using Group Search Optimizer (GSO), the affected parts of the OD, OC and RNFL are segmented. The proposed SDMFO-MBSVM method is executed in MATLAB site, its performance is analyzed with three existing methods. From the comparison, the accuracy of the proposed method in OD segmentation gives better results of 3.37%, 4.54% and 2.22%, OC segmentation gives better results of 2.22%, 3.37% and 4.54%, and RNFL segmentation gives the better results of 3.37%, 97.21% and 5.74%.

The early diagnosis of the glaucoma disease in the eye is crucial to avoid vision loss. This paper proposes an efficient computer-aided detection (CAD) system for diagnosing glaucoma based on fundus images, deep transfer learning and fuzzy aggregation operators. Specifically, the proposed CAD system includes three stages: (1) Detection of the region of interest of the optic disc using an efficient deep learning network, (2) Classification of images based on different pre-trained deep convolutional neural networks and support vector machines, and (3) Use of fuzzy aggregation operators to fuse the predictions of glaucoma classifiers. We used three popular yet robust aggregators: ordered weighted averaging (OWA) operator, weighted power mean (WPM), and exponential mean (EXM). We assessed the efficacy of the proposed glaucoma CAD system on three public datasets: DRISHTI-GS1, RIM-ONE, and REFUGE. The proposed conjunctive OWA aggregation method (Conj-OWA) achieves the best glaucoma classification results. Specifically, it achieves accuracy values of 90.2%, 97.8%, and 94.3% and area under the curve (AUC) values of 95.3%, 99.8%, and 96.2%, respectively, on DRISHTI-GS1, RIM-ONE, and REFUGE databases.

This article explains the various ocular surface diseases in the Asia Pacific region.

The article discusses about glaucoma as an old problem to new solutions. It touches on intraocular pressure, closed angle glaucoma and open angle glaucoma.

The article is about closed angle glaucoma in Asia Pacific region. It discusses briefly on the ophthalmic disease and its treatment.

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In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

Diabetic Retinopathy (DR) and Glaucoma are two of the most common causes of vision loss world-wide. However, it can be averted if therapy is begun early enough. In biomedical applications, the use of digital image processing has assisted in the automated identification of some ailments at an earlier stage. To make this prediction generally neural network classifier models were previously used, but these models have the drawback of being unable to detect multiple illnesses that occur in the eye at the same time and require a big database for successful classifier training. As a result, a model is needed to reliably distinguish DR and Glaucoma in diabetic individuals more accurately and with minimum dataset images. In this view, this study introduced Mayfly Optimized Deep Convolutional Network (MODCN) model for automated disease detection in the fundus retina images. In the MODCN model, the images are initially preprocessed, segmented at generator in the GAN model then a discriminator readily gives synthesis of real images of the fundus retina, thus a wide database has been created and considered as training images for the MODCN classifier. MODCN classifier has a modified high-density layer as a transition layer to avoid overfitting and the errors are minimized by tuning the hyperparameters using Mayfly Optimization Algorithm. After feature mapping, the classes normal, DR and Glaucoma are labeled and stored. At the testing stage, images are preprocessed, feature mapped and classified in the MODCN model. Thus, the proposed MODCN model detects multiple illness such as Diabetic Retinopathy and Glaucoma at the same time even with a small amount of database that performs a successful classifier training. This model is then evaluated and gives an accuracy of 99% that was higher compared to previous models.

Presence of fluids drives mechanical response in biological, soft hydrated tissues. A multiphase approach has been developed to allow for a fully-comprehensive, physically based view of their history-dependent response in static and dynamics. The related formulation considers both geometric and material non-linearities. In the present work, the non-linear aspects related to fluid flows are investigated. Low speed flows of liquid phase depend on the permeability of the deforming solid matrix; permeability is naturally related to void ratio, but further dependences on other variables can be explicitly added. Two different biomechanical problems are considered and the results are presented here. In the first case, a human lumbar intervertebral segment is investigated in healthy and degenerate condition: higher permeability and lower mechanical properties of disc affect the capability to withstand an imposed constant compressive load in time. The permeability is related only to void ratio and is not affected by the overpressure that dissipates with time. The second case refers to the trabecular meshwork in the eye; this tissue has a rule in the regulation of outflow of the aqueous humour from the eye: a variation in permeability determines obviously a variation of internal ocular pressure to guarantee the assumed constant outflow, and on the other side, pressure changes affect permeability distribution and thus final pressure changes. This effect is particularly significant with glaucoma, and thus the increment of intraocular pressure can be related to a progressive reduction in tissue permeability. The modelling of this effect can be improved by introducing a proper permeability-pressure relationship in addition to the permeability-void ratio dependency.

A theoretical model for corneal swelling with respect to aqueous inflow, based on corneal dimensions and mechanical and chemical properties, is presented in this paper. The mechanical properties of the corneal stroma, with respect to aqueous inflow, are considered in the basis of swelling mechanics. Equations describing the time course of swelling are also given. With this formulation, we can explain how glaucoma may result if outflow facility decreases faster compared to aqueous inflow. The opposite situation, e.g. of hypotony, may also be considered.

Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.

Purpose: Construct a finite element model of the human eye to quantitatively analyze the mechanical characteristics of the human eye, especially the glaucoma damage process of the optic nerve head (ONH). Method: First, the geometry model of the human eye with nonuniform thickness was established based on a reasonable hypothesis and assumptions. Because the ONH is an important factor for glaucoma, we refine the structure of the ONH with lamina cribrosa. Then, mesh division was applied for finite element analysis. To simplify the complexity of the analysis, the materials of the model were assumed to be isotropic linear elastic materials, and physical properties such as Young’s modulus and Poisson’s ratio were set according to published literature. Next, proper constraints and loads were applied to the model and solved with a finite element method. Result: A finite element model of the human eye was created to simulate the mechanical characteristics of the eye structures under high intraocular pressure (IOP). The ONH depressed 1.057mm under 0.009MPa pressure to simulate high IOP. Conclusion: The constructed model is able to quantitatively simulate excavation of the optic disc and damage of the optic nerve. The result proved Houcheng Liang’s hypothesis about the ONH damage mechanism in glaucoma.