Advertisement

Distinguising Proof of Diabetic Retinopathy Detection by Hybrid Approaches in Two Dimensional Retinal Fundus Images

  • Karkuzhali SEmail author
  • Manimegalai D
Patient Facing Systems
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

Abstract

Diabetes is characterized by constant high level of blood glucose. The human body needs to maintain insulin at very constrict range. The patients who are all affected by diabetes for a long time affected by eye disease called Diabetic Retinopathy (DR). The retinal landmarks namely Optic disc is predicted and masked to decrease the false positive in the exudates detection. The abnormalities like Exudates, Microaneurysms and Hemorrhages are segmented to classify the various stages of DR. The proposed approach is employed to separate the landmarks of retina and lesions of retina for the classification of stages of DR. The segmentation algorithms like Gabor double-sided hysteresis thresholding, maximum intensity variation, inverse surface adaptive thresholding, multi-agent approach and toboggan segmentation are used to detect and segment BVs, ODs, EXs, MAs and HAs. The feature vector formation and machine learning algorithm used to classify the various stages of DR are evaluated using images available in various retinal databases, and their performance measures are presented in this paper.

Keywords

Optic Disc Diabetic Retinopathy Hemorrhages Blood Vessels Microaneurysms Image Processing 

Notes

Compliance with ethical standards

Conflict of Interest

This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Zaki, W. M. D. W., Zulkifley, M. A., Hussain, A., Halim, W. H. W. A., Mustafa, N. B. A., and Ting, L. S., Diabetic retinopathy assessment: Towards an automated system. Biomedical Signal Processing and Control 24:72–82, 2016.CrossRefGoogle Scholar
  2. 2.
    Olson, J. L., Asadi-Zeydabadi, M., and Tagg, R., Theoretical estimation of retinal oxygenation in chronic diabetic retinopathy. Computers in Biology and Medicine 58:154–162, 2015.CrossRefGoogle Scholar
  3. 3.
    Yun, W. L., Acharya, U. R., Venkatesh, Y. V., Chee, C., Min, L. C., and Ng, E. Y. K., Identification of different stages of diabetic retinopathy using retinal optical images. Information Sciences 178(1):106–121, 2008.CrossRefGoogle Scholar
  4. 4.
    Akram, M. U., Khalid, S., and Khan, S. A., Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognition 46(1):107–116, 2013.CrossRefGoogle Scholar
  5. 5.
    Acharya, U. R., Mookiah, M. R. K., Koh, J. E., Tan, J. H., Bhandary, S. V., Rao, A. K., Fujita, H., Hagiwara, Y., Chua, C. K., and Laude, A., Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Computers in biology and medicine 75:54–62, 2016.CrossRefGoogle Scholar
  6. 6.
    Mahendran, G., and Dhanasekaran, R., Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Computers & Electrical Engineering 45:312–323, 2015.CrossRefGoogle Scholar
  7. 7.
    Imani, E., Pourreza, H. R., and Banaee, T., Fully automated diabetic retinopathy screening using morphological component analysis. Computerized Medical Imaging and Graphics 43:78–88, 2015.CrossRefGoogle Scholar
  8. 8.
    Figueiredo, I. N., Kumar, S., Oliveira, C. M., Ramos, J. D., and Engquist, B., Automated lesion detectors in retinal fundus images. Computers in biology and medicine 66:47–65, 2015.CrossRefGoogle Scholar
  9. 9.
    Mookiah, M. R. K., Acharya, U. R., Martis, R. J., Chua, C. K., Lim, C. M., Ng, E. Y. K., and Laude, A., Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowledge-based systems 39:9–22, 2013.CrossRefGoogle Scholar
  10. 10.
    Kumar, S. J. J., and Madheswaran, M., An improved medical decision support system to identify the diabetic retinopathy using fundus images. Journal of medical systems 36(6):3573–3581, 2012.CrossRefGoogle Scholar
  11. 11.
    Madheswaran, M., and Kumar, S. J. J., An Improved Medical Decision Support System To Grading The Diabetic Retinopathy Using Fundus Images. ICTACT Journal On Image and Video Processing 3(02):502–510, 2012.CrossRefGoogle Scholar
  12. 12.
    Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., and Van Ginneken, B., Ridge-Based Vessel Segmentation in Color Images of the Retina. IEEE Transactions on Medical Imaging 23(4):501–509, 2004.CrossRefGoogle Scholar
  13. 13.
    Hoover, A. D., Kouznetsova, V., and Goldbaum, M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 19(3):203–210, 2000.CrossRefGoogle Scholar
  14. 14.
    Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., Charton, B., and Klein, J.-C., Feedback on a publicly distributed database: the Messidor database. Image Analysis & Stereology 33(3):231–234, 2014.CrossRefGoogle Scholar
  15. 15.
    Kauppi, T, Kalesnykiene, V, Kamarainen, JK, Lensu, L, Sorri, I, Uusitalo, H, Kälviäinen, H & Pietilä, J 2006, 'DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms', Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland, pp.134.Google Scholar
  16. 16.
    Kauppi, T, Kalesnykiene, V, Kamarainen, J.-K, Lensu, L, Sorri, I, Raninen A, Voutilainen R, Uusitalo, H, Kälviäinen, H & Pietilä, J 2007, 'DIARETDB1 diabetic retinopathy database and evaluation protocol, in proceedings of the eleventh conference on Medical Image Understanding and Analysis, pp.1-10.Google Scholar
  17. 17.
    El Abbadi, N. K., and Al Saadi, E. H., Blood vessels extraction using Mathematical Morphology. Journal of Computer Science 9(10):1389–1395, 2013.CrossRefGoogle Scholar
  18. 18.
    Saleh, M. D., and Eswaran, C., An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection. Computer methods and programs in biomedicine 108(1):186–196, 2012.CrossRefGoogle Scholar
  19. 19.
    Dua, S., Acharya, U. R., Chowriappa, P., and Sree, S. V., Wavelet basedenergy features for glaucomatous image classification. IEEE transactions on information technology in biomedicine 16(1):80–87, 2012.CrossRefGoogle Scholar
  20. 20.
    Acharya, U. R., Mookiah, M. R. K., Koh, J. E., Tan, J. H., Noronha, K., Bhandary, S. V., Rao, A. K., Hagiwara, Y., Chua, C. K., and Laude, A., Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features. Computers in biology and medicine 73:131–140, 2016.CrossRefGoogle Scholar
  21. 21.
    Santhi, D., Manimegalai, D., Parvathi, S., and Karkuzhali, S., Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images. Biomedical Engineering/ Biomedizinische Technik 61(4):443–453, 2016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringKalasalingam Academy of Research and Education ( Deemed to be University)SrivilliputturIndia
  2. 2.Department of Information TechnologyNational Engineering CollegeKovilpattiIndia

Personalised recommendations