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Glaucoma is a progressive and deteriorating optic neuropathy that leads to visual field defects. The damage occurs as glaucoma is irreversible, so early and timely diagnosis is of significant importance. The proposed system employs the convolution neural network (CNN) for automatic segmentation of the retinal layers. The inner limiting membrane (ILM) and retinal pigmented epithelium (RPE) are used to calculate cup-to-disc ratio (CDR) for glaucoma diagnosis. The proposed system uses structure tensors to extract candidate layer pixels, and a patch across each candidate layer pixel is extracted, which is classified using CNN. The proposed framework is based upon VGG-16 architecture for feature extraction and classification of retinal layer pixels. The output feature map is merged into SoftMax layer for classification and produces probability map for central pixel of each patch and decides whether it is ILM, RPE, or background pixels. Graph search theory refines the extracted layers by interpolating the missing points, and these extracted ILM and RPE are finally used to compute CDR value and diagnose glaucoma. The proposed system is validated using a local dataset of optical coherence tomography images from 196 patients, including normal and glaucoma subjects. The dataset contains manually annotated ILM and RPE layers; manually extracted patches for ILM, RPE, and background pixels; CDR values; and eventually final finding related to glaucoma. The proposed system is able to extract ILM and RPE with a small absolute mean error of 6.03 and 5.56, respectively, and it finds CDR value within average range of ± 0.09 as compared with glaucoma expert. The proposed system achieves average sensitivity, specificity, and accuracies of 94.6, 94.07, and 94.68, respectively.

Glaucoma is a progressive and deteriorating optic neuropathy that leads to visual field defects. The damage occurs as glaucoma is irreversible, so early and timely diagnosis is of significant importance. The proposed system employs the convolution neural network (CNN) for automatic segmentation of the retinal layers. The inner limiting membrane (ILM) and retinal pigmented epithelium (RPE) are used to calculate cup-to-disc ratio (CDR) for glaucoma diagnosis. The proposed system uses structure tensors to extract candidate layer pixels, and a patch across each candidate layer pixel is extracted, which is classified using CNN. The proposed framework is based upon VGG-16 architecture for feature extraction and classification of retinal layer pixels. The output feature map is merged into SoftMax layer for classification and produces probability map for central pixel of each patch and decides whether it is ILM, RPE, or background pixels. Graph search theory refines the extracted layers by interpolating the missing points, and these extracted ILM and RPE are finally used to compute CDR value and diagnose glaucoma. The proposed system is validated using a local dataset of optical coherence tomography images from 196 patients, including normal and glaucoma subjects. The dataset contains manually annotated ILM and RPE layers; manually extracted patches for ILM, RPE, and background pixels; CDR values; and eventually final finding related to glaucoma. The proposed system is able to extract ILM and RPE with a small absolute mean error of 6.03 and 5.56, respectively, and it finds CDR value within average range of ± 0.09 as compared with glaucoma expert. The proposed system achieves average sensitivity, specificity, and accuracies of 94.6, 94.07, and 94.68, respectively.

Objective: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ratios from fundus and optical coherence tomography scans. However, this paper presents a novel strategy that pays attention to the RGC atrophy for screening glaucomatous pathologies and grading their severity. Methods: The proposed framework encompasses a hybrid convolutional network that extracts the retinal nerve fiber layer, ganglion cell with the inner plexiform layer and ganglion cell complex regions, allowing thus a quantitative screening of glaucomatous subjects. Furthermore, the severity of glaucoma in screened cases is objectively graded by analyzing the thickness of these regions. Results: The proposed framework is rigorously tested on publicly available Armed Forces Institute of Ophthalmology (AFIO) dataset, where it achieved the F1 score of 0.9577 for diagnosing glaucoma, a mean dice coefficient score of 0.8697 for extracting the RGC regions and an accuracy of 0.9117 for grading glaucomatous progression. Furthermore, the performance of the proposed framework is clinically verified with the markings of four expert ophthalmologists, achieving a statistically significant Pearson correlation coefficient of 0.9236. Conclusion: An automated assessment of RGC degeneration yields better glaucomatous screening and grading as compared to the state-of-the-art solutions. Significance: An RGC-aware system not only screens glaucoma but can also grade its severity and here we present an end-to-end solution that is thoroughly evaluated on a standardized dataset and is clinically validated for analyzing glaucomatous pathologies., Comment: Accepted in IEEE Transactions on Biomedical Engineering, Source Code: https://github.com/taimurhassan/rag-net-v2

Glaucoma is an asymptomatic neurological disease. It causes damage to optic nerve due to increased fluid pressure within eyes. In the proposed system, cup-to-disc-ratio has been computed considering internal layers of the retina using spectral-domain optical coherence tomography images. In the cup-diameter-calculation process, cup contour has been extracted from inner-limiting-membrane (ILM) layer. The paper introduces a new method to improve the precision of the ILM-layer extraction. It also employs a novel technique to refine contour of an ILM layer. The novel method has outperformed interpolation and Bezier curve fitting in term of outliers’ removal and surface refinement. In the disc-diameter-calculation process, the retinal-pigment-epithelium (RPE) layer end points have been used to define disc margin. Prior to RPE-layer extraction, ILM-Layer removal has been done by an innovative strategy to locate and remove ILM-layer. Finally, precise RPE-layer extraction has been done based on the novel thickness-value (TV) estimation method. Furthermore, a new criterion for cup edges determination, based on the mean value of RPE-layer end points, is proposed. The proposed system has shown a clear precedence over its contemporary systems in terms of accuracy and handling of acute cases. Satisfactory results have been obtained when compared with the clinical results.

Optical coherence tomography imaging modality mostly used in biomedical imaging, technique for the examination of retina, and becoming an essential tool for having a 3-D image of retina. Retinal layers analysis of an SD-OCT image facilities the diagnosis and monitoring of various ocular diseases such as Age related Degeneration, Macular Edema, Diabetic Retinopathy and glaucoma. Segmentation of retinal layers in an OCT Images has been a rigorous task due to speckle noise in an image. This paper presents a methods for the extraction of Inner Limiting Membrane (ILM) layer in extremely noisy OCT images. Compared with existing algorithms for the extraction of Inner Limiting Membrane layer (ILM). We also compared diagnostic accuracy of interpolating algorithms and aimed to suggest best interpolating algorithm for delineation of retinal layers focusing glaucoma detection for extremely noisy images. The ILM Layer and Retinal Epithelium Layer will determine the Cup to disc ratio (CDR) that is significant investigating factor for glaucoma diagnosis. The data set having 79 images, including normal, suspect and glaucoma subjects.