AN IN-DEPTH EXAMINATION OF DIVERSE IMAGE SEGMENTATION TECHNIQUES FOR FUNDUS IMAGING-BASED GLAUCOMA IDENTIFICATION

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.