An Improved Method for Automated Identification of Hard Exudates in Diabetic Retinopathy Disease

References

• N. S. Datta, H. S. Dutta, and K. Majumder, “An effective contrast enhancement method for identification of microaneurysms at early stage,” IETE. J. Res., Taylor & Francis, Vol. 62, pp. 1–10, Feb. 2016. doi: https://doi.org/10.1080/03772063.2015.1136573
   Google Scholar
• H. F. Jaffar, A. K. Nandi, and W. A. Nuaimy, “Descision support system for the detection and grading of hard exudates from color fundus photographs,” J. Biomed. Opt., Vol. 16, pp. 1–10, Nov. 2011.
   Web of Science ®Google Scholar
• K. B. Khan, A. A. Khaliq, A. jalil, M. A. Iftikhar, N. Ullah, M. W. Aziz, K. Ullah, and M. Shahid, “A review of retinal blood vessels extraction techniques: challenges, taxonomy and future trends,” Pattern Anal Appl., Vol. 21, no. 2, 1–36, Oct. 2018.
   Google Scholar
• A. Elbalaoui, and M. Fakir, “Exudates detection in fundus images using mean shift segmentation and adaptive thresholding,” Comput Methods Biomech Biomed Eng: Imaging Visualization, Taylor and Francis, Vol. 7, no. 2, 1–9, April. 2018.
   Google Scholar
• M. Rawat, and A. Singh, “A review of exudates detection using retinal images,” in Proc. 3rd IEEE Int. Conf. on CSGD, Delhi, 2016, pp. 4019–4022.
   Google Scholar
• D. Marin, A. Aquino, M. E. G. Arias, and J. M. Bravo, “A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants based features,” IEEE Trans Medical Imaging, Vol. 30, pp. 146–158, Jan. 2011. doi: https://doi.org/10.1109/TMI.2010.2064333
   PubMed Web of Science ®Google Scholar
• J. Kaur, and D. Mittal, “Segmentation and measurement of exudates in fundus images of the retina for detection of retinal disease,” J Biomed Eng Medl Imaging, Vol. 2, pp. 27–38, Feb. 2015.
   Google Scholar
• F. Aqeel, and G. Subramaniam, “Automated algorithm for retinal image exudates and drusens detection, segmentation and measurement”, in Proc. of IEEE Int. Conf. of Electro / information Technology, Milwaukee, WI, USA, 2014, pp. 206–215,
   Google Scholar
• A. G. Marupally, K. K. Vupparaboina, H. K. Peguda, and A. Richhariya, “Semi-automated quantification of hard exudates in colour fundus photographs diagnosed with diabetic retinopathy,” BMC Ophthalmol., Vol. 17, pp. 1–9, May. 2017. doi: https://doi.org/10.1186/s12886-017-0563-7
   PubMedGoogle Scholar
• P. H. Princye, and V. Vijayakumari, “Detection of exudates and feature extraction of retinal images using fuzzy clustering method,” in Proc. IEEE Int. Conf. of CIIT, Mumbai, 2013, pp. 388–394.
   Google Scholar
• P. Ravivarma, B. Ramasubramanian, G. Arunmani, and B. Babumohan, “An efficient system for the detection of exudates in color fundus images uses image processing technique,” in Proc. IEEE Int. Conf. ICACCCT, Ramanathapuram, pp. 1551–1553, 2014.
   Google Scholar
• A. Elbalaoui, M. Fakir, and A. Merbouha, “Segmentation and detection of Diabetic Retinopathy exudates,” Int. J. Comput. Appl., Vol. 91, pp. 7–13, April. 2014.
   Google Scholar
• G. S. A Grace, and S. K Mohideen, “Automatic detection of Optic disk and Exudates from retinal images using clustering algorithm,” in Proc of IEEE ISCO, Coimbatore, pp.1204–1210, 2013.
   Google Scholar
• S. Lu, and J. H. Lim, “Automatic Optic disk detection from retinal images by a line operator,” IEEE Trans. Biomed. Eng., Vol. 58, pp. 88–94, Oct. 2011.
   PubMed Web of Science ®Google Scholar
• D. Kayal, and S. Banerjee, “An approach to detect hard exudates using normalized cut image segmentation technique in digital retinal fundus images,” Adv Comput Sci, Eng Appl, Springer link, Vol. 166, pp. 123–128, May. 2016. doi: https://doi.org/10.1007/978-3-642-30157-5_13
   Google Scholar
• M. Kavitha, and S. Palani, “Hierarchical classifier for soft and hard exudates detection of retinal fundus images,” J. Intell. Fuzzy Syst., Vol. 27, pp. 2511–2529, Sep. 2014. doi: https://doi.org/10.3233/IFS-141224
   Web of Science ®Google Scholar
• R. C. Gonzalez, and R. E. Woods. Digital image processing. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2002.
   Google Scholar
• A. Mead, S. Burnett, and C. Davey, “Diabetic retinopathy screen in the UK,” J Royal Soc Med, Vol. 94, pp. 127–129, Mar. 2001. doi: https://doi.org/10.1177/014107680109400307
   PubMed Web of Science ®Google Scholar
• Decencière,  et al. “TeleOphta: Machine learning and image processing methods for teleophthalmology,” IRBM, Vol. 34, no. 2, 196–203, Apr 2013. doi:https://doi.org/10.1016/j.irbm.2013.01.010.
   Google Scholar
• T. Kauppi, V. Kalesnykiene, J. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. K¨alvïainen, and J. Pietil¨a, “DIARETDB1diabetic retinopathy database and evaluation protocol,” Proc. Medical Image Understanding and Analysis (MIUA), 61–65, 2007.
   Google Scholar
• X. Zhang, G. Thibault, E. Decenciere, B. Marcotegui, B. Lay, R. Danno, G. Cazuguel, et al., “Exudate detection in color retinal images for Mass screening of diabetic retinopathy,” Med. Image Anal., Vol. 18, pp. 1026–1043, Oct. 2014. doi: https://doi.org/10.1016/j.media.2014.05.004
   PubMed Web of Science ®Google Scholar
• W. Zhou, C. Wu, Y. Yi, and W. Du, “Automatic detection of exudates using super pixel multi-feature classification,” IEEE. Access., Vol. 5, pp. 17077–17088, Sep. 2017. doi: https://doi.org/10.1109/ACCESS.2017.2740239
   Google Scholar
• A. Rocha, T. Carvalho, H. F. Jelinek, S. Goldenstein, and J. Wainer, “Points of interest and visual dictionaries for automatic retinal lesion detection,” IEEE Trans. Biomed. Eng., Vol. 59, pp. 2244–2253, Aug. 2012. doi: https://doi.org/10.1109/TBME.2012.2201717
   PubMed Web of Science ®Google Scholar
• V. Das, and N. B. Puhan, “Tsallis entropy and sparse reconstructive dictionary learning for exudate detection in diabetic retinopathy,” J Medical Imaging, Vol. 4, no. 2, pp. 024002, April. 2017. doi: https://doi.org/10.1117/1.JMI.4.2.024002
   Google Scholar
• B. Harangi, and A. Hajdu, “Detection of exudates in fundus images using a Markovian segmentation model”, in Proc. 36th IEEE Int. Conf. on Medicine and Biology Society, Chicago, Aug 2014, pp. 1–6.
   Google Scholar
• D. Welfer, J. Scharcanski, and D. R. Marinho, “A coarse to fine strategy for automatically detecting exudates in color eye fundus images,” Comput Med Imaging Graphics, Vol. 34, pp. 228–235, April. 2010. doi: https://doi.org/10.1016/j.compmedimag.2009.10.001
   PubMedGoogle Scholar