Glaucoma Detection Using Image Channels and Discrete Wavelet Transform

References

• B. S. Kirar, and D. K. Agrawal, “Glaucoma diagnosis using discrete wavelet transform and histogram features from fundus image,” Int. J. Eng. Technol., Vol. 7, no. 4, pp. 2546–2551, 2018. doi: 10.14419/ijet.v7i4.14809
   Google Scholar
• U. R. Acharya, S. Bhat, J. E. Koh, S. V. Bhandary, and H. Adeli, “A novel algorithm to detect glaucoma risk using texton and local confguration pattern features extracted from fundus images,” Comput. Biol. Med., Vol. 88, pp. 72–83, 2017. doi: 10.1016/j.compbiomed.2017.06.022
   PubMed Web of Science ®Google Scholar
• Y. C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, and C. Y. Cheng, “Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis,” Ophthalmology, Vol. 121, no. 11, pp. 2081–2090, 2014. doi: 10.1016/j.ophtha.2014.05.013
   PubMed Web of Science ®Google Scholar
• Y. Hagiwara, J. E. W. Koh, J. H. Tan, S. V. Bhandary, A. Laude, E. J. Ciaccio, L. Tong, and U. R. Acharya, “Computer-aided diagnosis of glaucoma using fundus images: A review,” Comput. Methods Programs Biomed., Vol. 16, pp. 1–12, 2018. doi: 10.1016/j.cmpb.2018.07.012
   Google Scholar
• R. Kolar, and J. Jan, “Detection of glaucomatous eye via color fundus images using fractal dimensions,” Radio Eng., Vol. 17, no. 3, pp. 109–114, 2008.
   Google Scholar
• L. G. Nyúl, “Retinal image analysis for automated glaucoma risk evaluation,” Proc. SPIE 7497, MIPPR 2009 Med. Imaging Parall. Process. Images Optimiz. Tech., Vol. 7497, pp. 74971C-1-9, Oct. 2009.
   Google Scholar
• R. Bock, J. Meier, L. G. Nyul, J. Hornegger, and G. Michelson, “Glaucoma risk index: Automated glaucoma detection from color fundus images,” Med. Image Anal., Vol. 14, no. 3, pp. 471–481, 2010. doi: 10.1016/j.media.2009.12.006
   PubMed Web of Science ®Google Scholar
• C. Raja, and N. Gangatharan, “Glaucoma detection in fundal retinal images using trispectrum and complex wavelet based features,” Eur. J. Sci. Res., Vol. 97, no. 1, pp. 159–171, 2013.
   Google Scholar
• C. Raja, and N. Gangatharan, “Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses,” Int. J. Autom. Comput., Vol. 12, no. 4, pp. 393–401, 2015. doi: 10.1007/s11633-014-0858-6
   Web of Science ®Google Scholar
• C. Raja, and N. Gangatharan, “Optimal hyper analytic wavelet transform for glaucoma detection in fundal retinal images,” J. Electr. Eng. Technol., Vol. 10, no. 4, pp. 1899–1909, 2015. doi: 10.5370/JEET.2015.10.4.1899
   Web of Science ®Google Scholar
• M. Kavyashree, and P. V. Rao, “A novel approach on automatic detection of optic disc and optic cup segmentation,” ITSI Trans. Electr. Electron. Eng., Vol. 4, no. 2, pp. 2320–8945, 2016.
   Google Scholar
• S. Maheshwari, R. B. Pachori, and U. R. Acharya, “Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images,” IEEE J. Biomed. Health Inf., Vol. 21, no. 3, pp. 903–813, May 2017.
   Web of Science ®Google Scholar
• S. Maheshwari, R. B. Pachori, V. Kanhangad, S. V. Bhandary, and U. R. Acharya, “Iterative variational mode decomposition based automated detection of glaucoma using fundus images,” Comput. Biol. Med., Vol. 88, pp. 142–149, 2017. doi: 10.1016/j.compbiomed.2017.06.017
   PubMed Web of Science ®Google Scholar
• B. S. Kirar, and D. K. Agrawal, “Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images,” IET Image Proc., Vol. 13, no. 1, pp. 73–82, 2019. doi: 10.1049/iet-ipr.2018.5297
   Web of Science ®Google Scholar
• RIM-One fundus image database medical image analysis group. Available: http://medimrg.webs.ull.es/, 2018.
   Google Scholar
• K. Zuiderveld, “Contrast limited adaptive histograph equalization,” in Graphic gems IV, Andrew Glassner, Ed. San Diego: Academic Press Professional, 1994, pp. 474-485.
   Google Scholar
• T. S. Huang, G. J. Yang, and G. Y. Tang, “A fast two-dimensional median filtering algorithm,” IEEE Trans. Acoust. Speech Signal Process., Vol. 27, no. 1, pp. 13–18, 1979. doi: 10.1109/TASSP.1979.1163188
   Google Scholar
• R. C. Gonzalez, and R. E. Woods. Digital image processing. Noida: Pearson, 2014.
   Google Scholar
• F. Saki, A. Tahmasbi, H. S. Zadeh, and S. B. Shokouhi, “Opposite weight learning rules with application in breast cancer diagnosis,” Comput. Biol. Med., Vol. 43, no. 1, pp. 32–41, 2013. doi: 10.1016/j.compbiomed.2012.10.006
   PubMed Web of Science ®Google Scholar
• M. K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Inf. Theory, Vol. 8, no. 2, pp. 179–187, 1962. doi: 10.1109/TIT.1962.1057692
   Web of Science ®Google Scholar
• X. Wei. Chip histogram, Available: https://in.mathworks.com/matlabcentral/fileexchange/17537-histogram-features-of-a-gray-level-image?focused=5094859&tab=function, 2018.
   Google Scholar
• Y. Zhang, and W. Lenan, “An MR brain images classifier via principal component analysis and kernel support vector machine,” Prog. Electromagn. Res, Vol. 130, pp. 369–388, 2012. doi: 10.2528/PIER12061410
   Web of Science ®Google Scholar
• R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybernet., Vol. SMC-3, pp. 610–621, 1973. doi: 10.1109/TSMC.1973.4309314
   Web of Science ®Google Scholar
• A. Uppuluri. GLCM_Features4, Available: https://in.mathworks.com/matlabcentral/fileexchange/22354-glcm-features4-m–vectorized-version-of-glcm-features1-m–with-code-changes, 2018.
   Google Scholar
• W. O. Elferink. Gray level run length matrix (GLRLM), Available: https://in.mathworks.com/matlabcentral/fileexchange/52640-gray-level-run-length-image-statistics, 2018.
   Google Scholar
• F. Yuan, J. Shi, X. Xia, Y. Yang, Y. Fang, and R. Wang, “Sub oriented histograms of local binary patterns for smoke detection and texture classification,” KSII Trans. Internet Inf. Syst., Vol. 10, no. 4, pp. 1807–1823, 2016.
   Web of Science ®Google Scholar
• I. Kononenko. Estimating attributes: analysis and extensions of RELIEF. Berlin: Springer, 1994.
   Google Scholar
• G. W. Stewart, “On the early history of the singular value decomposition,” SIAM Rev., Vol. 35, no. 4, pp. 551–566, 1993. doi: 10.1137/1035134
   Web of Science ®Google Scholar
• J. A. K. Suykens, and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Process. Lett., Vol. 9, no. 3, pp. 293–300, 1999. doi: 10.1023/A:1018628609742
   Web of Science ®Google Scholar
• N. N. Kulkarni, and V. K. Bairagi, “Extracting salient features for EEG based diagnosis of Alzheimer’s disease using support vector machine classifier,” IETE. J. Res., Vol. 63, no. 1, pp. 11–22, Jan. 2017. doi: 10.1080/03772063.2016.1241164
   Web of Science ®Google Scholar
• B. S. Kirar, and D. K. Agrawal, “Comparison between empirical and variational mode decomposition based on percentage variation in entropy feature from glaucoma image,” Indian J. Public Health Res. Develop., Vol. 9, no. 9, pp. 10–15, Sep. 2018. doi: 10.5958/0976-5506.2018.00960.9
   Google Scholar