General Awareness and Knowledge about Glaucoma Cataracts for Diabetic

  • Authors

    • Nawar Banawan Hassan Al-Kanany
    • LUBNA EMAD KADHIM
    https://doi.org/10.14419/ijet.v7i3.30.19090
  • .
  • Abstract

    Horizontals and verticals cups to discs percentages are the most crucial limitations used clinically to find glaucoma or observer its growth and are physically calculated from pictures of retinal fundus of the optic nerves heads. Appointed to the rarity of the glaucoma specialists as well as the rising of glaucoma’s population, an automatically analyzed horizontal and vertical cup to disc ratios (HCDRs and VCDRs, resp.) may be valuable for glaucoma monitoring. We description one algorithm to determine the HCDRs and VCDRs. This algorithms, amount and pictures techniques were technologically advanced for segmenting the disc, though thresholding operating Type-II fuzzy method was progressed for slices of the cup. The outcomes from the algorithms were confirmed using the guide of images markings from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by five ophthalmologists. The algorithm’s accuracy for HCDRs and VCDRs merged was 65.3 %. Simply the accuracy of guide markings by one ophthalmologist was higher than the algorithm’s accuracy. The algorithm’s best understanding was with markings by ophthalmologist number 1 in 130 images (32.7%) of the whole examined images. Flow chart for images analyzed were  add in this paper as reference.

  • References

    1. [1] F. Aguiree, A. Brown, N.H. Cho, G. Dahlquist, S. Dodd, T. Dunning, et al.IDF diabetes atlas (7Th ed.), International Diabetes Federation (2015)

      [2] M.K. Ikram, J.C. Witteman, J.R. Vingerling, M.M. Breteler, A. Hofman, de Jong P.T.Retinal vessel diameters and risk of hypertension: the Rotterdam study Hypertens, 47 [2] (2016), pp. 189-194

      [3] C.Y. luiheung, Y. Zheng, W. Hsu, M.L. Lee, Q.P. Lau, P. Mitchell, J.J. Wang, R. Klein, Wong T.Y.Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors Ophthalmol, 118 [5] (2011), pp. 812-818

      [4] Liu J., Yin F. S., Wong D. W. K., et al. Automatic glaucoma diagnosis from fundus image. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011; Boston, MA, USA. pp. 3383–3386

      [5] Banister K., Boachie C., Bourne R., et al. Can automated imaging for optic disc and retinal nerve fiber layer analysis aid glaucoma detection? Ophthalmology. 2016;123[5]:930–938. doi: 10.1016/j.ophtha.2016.01.041

      [6] Sedai S., Roy P. K., Mahapatra D., Garnavi R. Segmentation of optic disc and optic cup in retinal fundus images using shape regression. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2016; Orlando, FL, USA. pp. 3260–3264.

      [7] Díaz-Pernil D., Fondón I., Peña-Cantillana F., Gutiérrez-Naranjo M. A. Fully automatized parallel segmentation of the optic disc in retinal fundus images. Pattern Recognition Letters. 2016;83:99–107. doi: 10.1016/j.patrec.2016.04.025

      [8] Naser Langroudi M., Sadjedi H. A new method for automatic detection and diagnosis of retinopathy diseases in colour fundus images based on morphology. 2010 International Conference on Bioinformatics and Biomedical Technology; 2010; Chengdu, China. pp. 134–138

      [9] Xu X., Niemeijer M., Song Q., et al. Vessel boundary delineation on fundus images using graph-based approach. IEEE Transactions on Medical Imaging. 2011;30[6]:1184–1191. doi: 10.1109/TMI.2010.2103566

      [10] Kumar J. R. H., Pediredla A. K., Seelamantula C. S. Active discs for automated optic disc segmentation. 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP); 2015; Orlando, FL, USA. pp. 225–229

      [11] Almazroa A., Sun W., Alodhayb S., Raahemifar K., Lakshminarayanan V. Optic disc segmentation: level set methods and blood vessels inpainting. Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. SPIE Medical Imaging, article 1013806; 2017; Orlando, Florida, USA

      [12] Salih N. D., Saleh M. D., Eswaran C., Abdullah J. Fast optic disc segmentation using FFT-based template-matching and region-growing techniques. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2017

      [13] Akyol K., Åžen B., Bayir Åž. Automatic detection of optic disc in retinal image by using keypoint detection, texture analysis, and visual dictionary techniques. Computational and Mathematical Methods in Medicine. 2016

      [14] Fondon I., Valverde J. F., Sarmiento A., Abbas Q., Jimenez S., Alemany P. Automatic optic cup segmentation algorithm for retinal fundus images based on random forest classifier. IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON); 2015; Salamanca, Spain. pp. 1–6

  • Downloads

  • How to Cite

    Banawan Hassan Al-Kanany, N., & EMAD KADHIM, L. (2018). General Awareness and Knowledge about Glaucoma Cataracts for Diabetic. International Journal of Engineering & Technology, 7(3.30), 204-210. https://doi.org/10.14419/ijet.v7i3.30.19090

    Received date: 2018-09-06

    Accepted date: 2018-09-06