1 |
S. Kar, and S. Maity. Detection of neovascularization in retinal images using mutual information maximization. Computers and Electrical Engineering, 62:1-15, August 2017.
DOI
|
2 |
J. Tan, H. Fujita, S. Sivaprasad, S. Bhandary, A. Rao, K. Chua, and U. Acharya. Automated Segmentation of Exudates, Haemorrhages, Microaneurysm susing Single Convolutional Neural Network. Information Sciences, 420 :66-76, December 2017.
DOI
|
3 |
A. Floriano, A. Santiago, O. Nieto, and C. Marquez. A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images. Computers & Electrical Engineering, available online, November 2017.
|
4 |
J. Medhi and S. Dandapat. An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images. Computers in Biology and Medicine, 74:30-44, July 2016.
DOI
|
5 |
J. Cheng, J. Liu, Y. Xu, F. Yin, D. Wong, N. Tan, D. Tao, C. Cheng, T. Aung, and T. Wong. Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening. IEEE Trans. on Medical Imaging, 32:1019-1032, June 2013.
DOI
|
6 |
S. Devi, K. Ramachandran, and A. Sharma. Retinal Vasculature Segmentation in Smartphone Ophthalmoscope Images. Proceedings of 7th WACBE World Congress on Bioengineering, 52:64-67, 2015.
|
7 |
M. Blanckenberg, C. Worst and C. Scheffer. Development of a Mobile Phone Based Ophthalmoscope for Telemedicine. Proceedings of the IEEE Engineering in Medicine and Biology Conference, Massachusetts, 5236-5239, 2011.
|
8 |
S. Wang, K. Jin, H. Lu, C. Cheng, J. Ye, and D. Qian. Human visual system-based fundus image quality assessment of portable fundus camera photographs. IEEE Trans. on Medical Imaging, 35:1046 - 1055, April 2016.
DOI
|
9 |
A. Russo, F. Morescalchi, C. Costagliola, L. Delcassi, and F. Semeraro. A Novel Device to Exploit the Smartphone Camera for Fundus Photography. Journal of Ophthalmology, Article ID 823139, 2015.
|
10 |
A. Russo, F. Morescalchi, C. Costagliola, L. Delcassi, and F. Semeraro. Comparison of Smartphone Ophthalmoscopy With Slit-Lamp Biomicroscopy for Grading Diabetic Retinopathy. American Journal of Ophthalmology, 159:360-364, February 2015.
DOI
|
11 |
M. Dobes, L. Machala, and T. Furst. Blurred image restoration: A fast method of finding the motion length and angle. Digital Signal Processing, 20:1677-1686, December 2010.
DOI
|
12 |
J. Cai, H. Ji, C. Liu, and Z. Shen. Blind motion deblurring using multiple images. Journal of Computational Physics, 228:5057-5071, August 2009.
DOI
|
13 |
Gegundez-Arias, M. E., Marin, D., Bravo, J. M., and Suero, A. Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques. Computerized Medical Imaging and Graphics, 37:386-393, September 2013.
DOI
|
14 |
A. Deshpande, and S. Patnaik. Single image motion deblurring: An accurate PSF estimation and ringing reduction. Optik, 125:3612-3618, July 2014.
DOI
|
15 |
H. Lidong, Z. Wei, W. Jun, and S. Zebin. Combination of contrast limited adaptive histogram equalization and discrete wavelet transform for image enhancement. IET Image Processing, 9:908 - 915, October 2015.
DOI
|
16 |
Smartphone-Captured Retinal Image Database, https://sites.google.com/site/yaroubelloumi/retinal-images, 2017.
|