Segmentation techniques for computer-aided diagnosis of glaucoma: A review

Sumaiya Pathan, Preetham Kumar, Radhika M. Pai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Machine Learning and Data Science - Recent Achievements and Research Directives
PublisherSpringer Verlag
Pages163-173
Number of pages11
ISBN (Print)9789811085680
DOIs
Publication statusPublished - 01-01-2018
Event1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017 - Goa, India
Duration: 25-10-201727-10-2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume705
ISSN (Print)2194-5357

Conference

Conference1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017
CountryIndia
CityGoa
Period25-10-1727-10-17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Pathan, S., Kumar, P., & Pai, R. M. (2018). Segmentation techniques for computer-aided diagnosis of glaucoma: A review. In Advances in Machine Learning and Data Science - Recent Achievements and Research Directives (pp. 163-173). (Advances in Intelligent Systems and Computing; Vol. 705). Springer Verlag. https://doi.org/10.1007/978-981-10-8569-7_18