A study to identify limitations of existing automated systems to detect glaucoma at initial and curable stage. Issue 3 (5th January 2021)

• Record Type: Journal Article
• Authors: Khalil, Tehmina; Akram, Muhammad Usman; Khalid, Samina; Dar, Saadat Hanif; Ali, Nouman
• Is Part Of: International journal of imaging systems and technology. Volume 31:Issue 3(2021)
• Page Start: 1155
• Page End: 1173
• Publication Date: 2021-01-05
• Subjects: glaucoma diagnosis -- image processing -- machine learning -- opthalmic imaging technologies; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22541
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Glaucoma ocular disease is the second topmost reason for irreversible visual impairment around the world. This malady can be cured and permanent blindness can be prevented by timely diagnosis and treatment. This study is an attempt to analyse the current status of automated glaucoma diagnosis systems. Existing systems have been analysed on the base of ophthalmic imaging technology, capability to detect glaucoma at initial or latter stages, detection strategy and performance. Analysis revealed several research gaps mainly in automated glaucoma detection based on Fundus and Optical Coherence Tomography (OCT) ophthalmic imaging technologies. More accurate diagnosis of glaucoma at early and curable stages is possible by bridging the identified gaps.