Glaucoma detection using image processing techniques: A literature review. (December 2019) - Sarhan, Abdullah; Rokne, Jon; Alhajj, Reda
Highlights: A summary of image analysis approaches for glaucoma detection, using both 2D and 3D images. Approaches for segmenting different objects from retinal images have been identified. Approaches for detection of glaucoma using retinal images have been identified. Challenges faced by researchers working in this domain are addressed. A list of currently available datasets for glaucoma research have been identified and evaluation methods. Abstract: The term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows: approaches to segment different objects from both 2D and 3D images; approaches that may lead to encouraging results for glaucoma detection; challenges faced by researchers; and currently available retinal datasets and evaluation methods.