1. Siamak YG, Michael H, Madhusudhanan B, Tzyy-Ping J, Robert WN, Felipe MA, Linda ZM, Jeffrey LM, Christopher GA, Christopher B (2013) Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points. IEEE Trans Biomed Eng 61(4):1143–1154
2. Cheng J, Liu J, Xu Y, Yin F, Tan NM, Wong WKD, Lee BH, Cheng X, Gao X, Zhang Z, Wong TY (2017) Methods and systems for automatic location of optic structures in an image of an eye, and for automatic retina cup-to-disc ratio computation. US Patent 9:684,959
3. Martins J, Cardoso JS, Soares F (2020) Offline computer-aided diagnosis for glaucoma detection using fundus images targeted at mobile devices. Comput Methods Prog Biomed 192:105341
4. Kourkoutas D, Karanasiou IS, Tsekouras GJ, Moshos M, Iliakis E, Georgopoulos G (2012) Glaucoma risk assessment using a non-linear multivariable regression method. Computer Methods Programs Biomed 108(3):31149–31159
5. Singh, L. K., & Garg, H. (2019). Detection of glaucoma in retinal fundus images using fast fuzzy C means clustering approach. In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 397-403). IEEE