Pathan, Sumaiya and Kumar, Preetham and Pai, Radhika M (2017) Segmentation Techniques for Computer-Aided Diagnosis of Glaucoma: A Review. In: Latest Advances in Machine learning and DAta Science , LAMDA 2017, 2017, National Institute of Technology, Goa.

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Abstract

Glaucoma is an eye disease in which the optic nerve head (ONH) is damaged, leading to irreversible loss of vision. Vision loss due to glaucoma can be prevented only if it is detected at an early stage. Early diagnosis of glaucoma is possible by measuring the level of intra-ocular pressure (IOP) and the amount of neuro-retinal rim (NRR) area loss. The diagnosis accuracy depends on the experience and domain knowledge of the ophthalmologist. Hence, automated extraction of features from the retinal fundus images can play a major role for screening of glaucoma. The main aim of this paper is to review the different segmentation algorithms used to develop a computer-aided diagnostic (CAD) system for the detection of glaucoma from fundus images, and additionally, the future work is also highlighted.

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