Peripapillary Atrophy Segmentation and Classification Methodologies for Glaucoma Image Detection: A Review

Information-based image processing and computer vision methods are utilized in several healthcare organizations to diagnose diseases. The irregularities in the visual system are identified over fundus images with a fundus camera. Among ophthalmology diseases, glaucoma is the most common case leading to neurodegenerative illness. The unsuitable fluid pressure inside the eye within the visual system is described as the major cause of those diseases. Glaucoma has no symptoms in the early stages, and if it is not treated, it may result in total blindness. Diagnosing glaucoma at an early stage may prevent permanent blindness. Manual inspection of the human eye may be a solution, but it depends on the skills of the individuals involved. The diagnosis of glaucoma by applying a consolidation of computer vision, artificial intelligence, and image processing can aid in the prevention and detection of those diseases. In this review article, we aim to introduce numerous approaches based on peripapillary atrophy segmentation and classification that can detect these diseases, as well as details regarding the publicly available image benchmarks, datasets, and measurement of performance. The review article highlights the research carried out on numerous available study models that objectively diagnose glaucoma via peripapillary atrophy from the lowest level of feature extraction to the current direction based on deep learning. The advantages and disadvantages of each method are addressed in detail, and tabular descriptions are included to highlight the results of each category. Moreover, the frameworks of each approach and fundus image datasets are provided. Our study would help in providing possible future work directions to diagnose glaucoma.