Computer-aided diagnosis of glaucoma using fundus images: A review. (October 2018)

• Record Type: Journal Article
• Authors: Hagiwara, Yuki; Koh, Joel En Wei; Tan, Jen Hong; Bhandary, Sulatha V.; Laude, Augustinus; Ciaccio, Edward J.; Tong, Louis; Acharya, U. Rajendra
• Is Part Of: Computer methods and programs in biomedicine. Volume 165(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-10
• Subjects: Computer-aided detection system -- Deep learning -- Glaucoma -- Machine learning -- Optic disc -- Segmentation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.07.012
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Review on existing techniques to diagnose glaucoma automatically. Discussed the building blocks of a CAD system. Deep learning (DL) methods are effective in glaucoma diagnosis. Novel DL algorithms and big data are needed to develop accurate CAD system. Abstract: Background and objectives: Glaucoma is an eye condition which leads to permanent blindness when the disease progresses to an advanced stage. It occurs due to inappropriate intraocular pressure within the eye, resulting in damage to the optic nerve. Glaucoma does not exhibit any symptoms in its nascent stage and thus, it is important to diagnose early to prevent blindness. Fundus photography is widely used by ophthalmologists to assist in diagnosis of glaucoma and is cost-effective. Methods: The morphological features of the disc that is characteristic of glaucoma are clearly seen in the fundus images. However, manual inspection of the acquired fundus images may be prone to inter-observer variation. Therefore, a computer-aided detection (CAD) system is proposed to make an accurate, reliable and fast diagnosis of glaucoma based on the optic nerve features of fundus imaging. In this paper, we reviewed existing techniques to automatically diagnose glaucoma. Results: The use of CAD is very effective in the diagnosis of glaucoma and can assist the clinicians to alleviate their workload significantly. We have also discussed the advantages of employing state-of-art techniques, including deep learning (DL), when developing the automated system. The DL methods are effective in glaucoma diagnosis. Conclusions: Novel DL algorithms with big data availability are required to develop a reliable CAD system. Such techniques can be employed to diagnose other eye diseases accurately. Graphicalabstract :