A Novel IR Analyzer Based Property Extraction for Segmented Branch Retinal Artery Occlusion and GWO-CNN Based Classification – An Ophthalmic Outcome

REFERENCES

• R. K. Murthy, S. Grover, and K. V. Chalam, “Sequential spectral domain OCT documentation of retinal changes after branch retinal artery occlusion,” Clinical Ophthalmology (Auckland, N.Z.), Vol. 4, pp. 327–9, 2010.
   PubMedGoogle Scholar
• S. Rumelt, and G. C. Brown, “Update on treatment of retinal arterial occlusions,” Curr. Opin. Ophthalmol., Vol. 14, no. 3, pp. 139–141, 2003.
   PubMedGoogle Scholar
• C. M. F. M. Parcero, B. D. P. Freitas, E. F. Marback, O. d. O. Maia Júnior, and R. L. Marback, “Optical coherence tomography findings in acute phase of branch retinal artery occlusion: case report,” Arq. Bras. Oftalmol., Vol. 73, pp. 189–92, 2010.
   PubMed Web of Science ®Google Scholar
• S. S. Hayreh, “Acute retinal arterial occlusive disorders,” Prog. Retinal Eye Res., Vol. 30, no. 5, pp. 359–94, 2011.
   PubMed Web of Science ®Google Scholar
• S. Clare, “A case report of branch retinal artery occlusion in a teenager due to hyper homocysteinaemia; the interplay of genetic and nutritional defects,” BMC Ophthalmol., Vol. 18, no. Suppl 1, pp. 220, 2018. doi:10.1186/s12886-018-0859-2.
   PubMedGoogle Scholar
• A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and R. Badrinath, “Rapid automated Tracing from retinal fundus images using direct exploratory algorithms,” IEEE Trans. Inf. Technol. Biomed., Vol. 3, no. 2, pp. 125–38, Jun. 1999.
   PubMedGoogle Scholar
• A. D. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a Matched filter response,” IEEE Trans. Med. Imaging, Vol. 19, no. 3, pp. 203–10, Mar. 2000.
   PubMed Web of Science ®Google Scholar
• E. J. Carmona, M. Rincon, J. Garcia-feijoo, and J. M. Martinez-de-la-casa, “Identification of the optic nerve head with genetic algorithm,” Aritificial Intelligence in Medicine, Vol. 43, no. 3, pp. 243–59, Jul. 2008.
   PubMed Web of Science ®Google Scholar
• S. Joes, D. A. Michael, N. Medindert, M. A. Viergever, and V. G. Bram, “Ridge-Based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging, Vol. 23, no. 4, pp. 501–9, Apr. 2004.
   PubMed Web of Science ®Google Scholar
• G. Zhijun, B. Wei, W. Xiangqian, and Y. Zheng, “Automated layer segmentation of macular OCT images via graph based SLIC superpixels and manifold ranking approach,” Comput. Med. Imaging Graph., Vol. 55, pp. 42–53, Jan. 2017.
   PubMed Web of Science ®Google Scholar
• J. D. hemanth, J. Anitha, v. Kezi selva, and I. A. selvakumar, “Performance enhanced PSO based modified kohonen neural network for retinal image classification,” J. Chin. Inst. Eng., Vol. 35, no. 8, pp. 979–91, Dec. 2012.
   Web of Science ®Google Scholar
• R. Badgujar, and P. Deore, “MBO-SVM based exudates classification in funds retinal images of diabetic patients,” Comput. Methods Biomech. Biomed. Eng. Imag. Visual., Vol. 7, no. 2, pp. 195–206, 2019.
   Web of Science ®Google Scholar
• G. Balkys, and G. Dzemyda, “Segmenting the eye funds images for identification of blood vessels,” Math. Model. Anal., Vol. 17, no. 1, pp. 21–30, Feb. 2012.
   Web of Science ®Google Scholar
• T. Dutta, O. Bandyopadhyay, N. Dutta, A. Biswas, and B. B. Bhattacharya, “Structural feature analysis of the vascular network in retinal images,” Comput. Methods Biomech. Biomed. Eng. Imag. Visual., Vol. 7, no. 1, pp. 37–48, 2019.
   Web of Science ®Google Scholar
• S. N. Randive, R. K. Senapati, and A. D. Rahulkar, “A review on computer aided recent developments for automatic detection of diabetic retinopathy”,” J. Med. Eng. Technol., Vol. 43, no. 2, pp. 87–99, Jun. 2019.
   PubMedGoogle Scholar
• A. Elbalaoui, and M. Fakir, “Exudates detection in funds images using mean- shift segmentation and adaptive thresholding,” Comput. Methods Biomech. Biomed. Eng. Imag. Visual., Vol. 7, no. 2, pp. 145–53, 2019.
   Web of Science ®Google Scholar
• K. C. Jordan, M. Menolotto, N. M. Bolster, L. A. T. Livingstone, and E. G. Mario, “A review of feature based retinal image analysis,” Expert. Rev. Ophthalmol., Vol. 12, no. 3, pp. 207–20, Mar. 2017.
   Web of Science ®Google Scholar
• N. Nita, and M. Milind, “Extraction of blood vessels and optic disc in retinal images”,” Comput. Methods Biomech. Biomed. Eng. Imag. Visual., Vol. 6, no. 1, pp. 31–42, 2018.
   Web of Science ®Google Scholar
• V. Kajic, P. Boris, B. Hermann, B. Hofer, D. Marshall, P. L. Rosin, and D. Wolfgang, “Roboust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis,” Opt. Express, Vol. 18, no. 14, pp. 14730, Jul. 2010.
   PubMed Web of Science ®Google Scholar
• S. P. Maity, and S. K. Sudeshna, “Automatic detection of retinal lesions for screening of diabetic retinopathy,” IEEE Trans. Biomed. Eng., Vol. 65, no. 3, pp. 608–618, Mar. 2018.
   PubMed Web of Science ®Google Scholar
• M. Niemeijer, B. V. Ginneken, J. Staal, M. S. A. Suttorp-schulten, and M. D. Abramoff, “Automatic detection of Red lesions in Digital color fundus photographs,” IEEE Trans. Med. Imaging, Vol. 24, no. 5, pp. 584–92, May 2005.
   PubMed Web of Science ®Google Scholar
• S. Fei, G. Jingyun, X. Dehui, C. Haoyu, and C. Xinjian, “A framework for classification and segmentation of branch retinal artery occlusion in SD-OCT,” IEEE Trans. Image Process., Vol. 26, no. 7, pp. 3518–27, 2017.
   PubMed Web of Science ®Google Scholar
• F. Girard, C. Kavalec, and F. Cheriet. “Joint Segmentation and classification of retinal arteries/veins from funds image” Preprint submitted to Elsevier, March 5, 2019.
   Google Scholar
• T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “A Contribution of image processing to the diagnosis Of diabetic retinopathy—detection of exudates in Color fundus images of the Human retina,” IEEE Trans. Med. Imaging, Vol. 21, no. 10, pp. 1236–43, OCT. 2002.
   PubMed Web of Science ®Google Scholar
• B. Dashtbozorg, J. Zhang, F. Huang, and B. M. ter Haar Romeny, “Retinal microaneurysms detection using local convergence index features,” IEEE Trans. Image Process., Vol. 27, no. 7, pp. 3300–3315, 2018.
   PubMed Web of Science ®Google Scholar
• S. Roychowdhury, D. D. Koozekanani, and K. K. Parhi, “Blood vessel segmentation of fundus images by major vessel extraction and subimage classification,” IEEE J. Biomed. Health Inform., Vol. 19, no. 3, pp. 1118–28, 2014. doi:10.1109/JBHI.2014.2335617.
   Web of Science ®Google Scholar
• M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, and S. A. Barman, “An ensemble classification-based approach Applied to retinal blood vessel segmentation,” IEEE Trans. Biomed. Eng., Vol. 59, no. 9, pp. 2538–48, Sep. 2012.
   PubMed Web of Science ®Google Scholar
• A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag., Vol. 30, no. 2, pp. 484–496, 2010. doi:10.1109/TMI.2010.2087390.
   PubMed Web of Science ®Google Scholar
• https://imagebank.asrs.org/
   Google Scholar
• S. G. Gayathri, and S. Joseph Jawhar, “Segmentation and classification methods in retinal examination- A survey,” Int. J. Res. Anal. Rev. (IJRAR), Vol. 5, no. 3, pp. 514–29, Aug. 2018.
   Google Scholar