Asian Journal of Information Technology
Year:
2016
Volume:
15
Issue:
14
Page No.
2464 - 2472
References
Akram, M.U., S. Khalid and S.A. Khan, 2013. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit., 46: 107-116.
CrossRef | Direct Link | Gowthaman, R., 2014. Automatic identification and classification of microaneurysms for detection of diabetic retinopathy. IJRET. Int. J. Res. Eng. Technol., 3: 464-473.
Direct Link | Huang, G.B., H. Zhou, X. Ding and R. Zhang, 2012. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42: 513-529.
CrossRef | Direct Link | Marin, D., A. Aquino, M.E. Gegundez-Arias and J.M. Bravo, 2011. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imag., 30: 146-158.
CrossRef | Direct Link | Nandhini, E. and S.R. Malathi, 2014. Location of Fovea centralis In digital fundus images using adaptive thresholding method. Int. J. Pharma Bio Sci., 5: 590-600.
Paintamilselvi, S., 2012. A novel method to detect the fovea of fundus retinal image. Int. J. Res. Dev. Eng. IJRDE., 1: 21-25.
Samanta, S., S.K. Saha and B. Chanda, 2011. A simple and fast algorithm to detect the fovea region in fundus retinal image. Proceedings of the 2nd International Conference on Emerging Applications of Information Technology, February 19-20, 2011, Kolkata, India, pp: 206-209.
Sundhar, C. and D. Archana, 2014. Automatic screening of fundus images for detection of diabetic retinopathy. Int. J. Commun. Comput. Technol., 2: 100-105.
Varalakshmi, V.R. and P. Janardhan, 2014. Detection of fovea region in fundus retinal image using wavelet transform and fuzzy c-means clustering. Int. J. Electr. Electron. Data Commun. IJEEDC., 2: 13-16.