Journal of Engineering and Applied Sciences
Year:
2016
Volume:
11
Issue:
11
Page No.
2378 - 2382
References
Azemin, C., M. Zulfaezal and M. Hilmi, 2014. Supervised pterygium fibrovascular redness grading using generalized regression neural network. N. Trends Software Methodologies Tools Tech., 2014: 650-656.
Fieguth, P. and T. Simpson, 2002. Automated measurement of bulbar redness. Invest. Ophthalmol. Visual Sci., 43: 340-347.
Direct Link | Haralick, R.M., K. Shanmugam and I.H. Dinstein, 1973. Textural features for image classification. IEEE Trans. Syst. Man Cybern., SMC-3: 610-621.
CrossRef | Direct Link | Mahar, P.S. and N. Manzar, 2013. Pterygium recurrence related to its size and corneal involvement. J. Col. Physicians Surg. Pak, 23: 120-123.
Murphy, P.J., J.S.C. Lau, M.M.L. Sim and R.L. Woods, 2007. How red is a white eye? Clinical grading of normal conjunctival hyperaemia. Eye, 21: 633-638.
Direct Link | Peng, H., F. Long and C. Ding, 2005. Feature selection based on mutual information criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 27: 1226-1238.
CrossRef | Direct Link | Schulze, M.M., N. Hutchings and T.L. Simpson, 2011. Grading bulbar redness using cross-calibrated clinical grading scales. Invest. Ophthalmol. Visual Sci., 52: 5812-5817.
CrossRef | Direct Link | Tan, D.T., S.P. Chee, K.B. Dear and A.S. Lim, 1997. Effect of pterygium morphology on pterygium recurrence in a controlled trial comparing conjunctival autografting with bare sclera excision. Arch. Ophthalmol., 115: 1235-1240.
Trucco, E., A. Ruggeri, T. Karnowski, L. Giancardo and E. Chaum
et al., 2013. Validating retinal fundus image analysis algorithms: Issues and a proposalvalidating retinal fundus image analysis algorithms. Invest. Ophthalmol. Visual Sci., 54: 3546-3559.
Direct Link | Wu, S., J. Hong, L. Tian, X. Cui and X. Sun
et al., 2015. Assessment of bulbar redness with a newly developed keratograph. Optometry Vision Sci., 92: 892-899.
CrossRef | Direct Link |