Localization of Region of Interest from Retinal Fundus Image for Early Detection of Glaucoma
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3299-9_13
Reference17 articles.
1. Quigley H, Broman AT (2006) The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol 90:262–267. https://doi.org/10.1136/bjo.2005.081224
2. Dimitriou CD, Broadway DC (2013) Pathophysiology of glaucoma. Glaucoma Basic Clin Perspect 33–57. https://doi.org/10.2217/EBO.12.421
3. Chaikitmongkol V, Khunsongkiet P, Patikulsila D et al (2018) Color fundus photography, optical coherence tomography, and fluorescein angiography in diagnosing polypoidal choroidal vasculopathy. Am J Ophthalmol 192:77–83. https://doi.org/10.1016/j.ajo.2018.05.005
4. Hussain SA, Holambe AN (2015) Automated detection and classification of glaucoma from eye fundus images: a survey. Int J Comput Sci Inf Technol 6:1217–1224
5. Bock R, Meier J, Nyúl LG et al (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14:471–481. https://doi.org/10.1016/j.media.2009.12.006
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