Advertisement

A Simple Algorithm for Hard Exudate Detection in Diabetic Retinopathy Using Spectral-Domain Optical Coherence Tomography

  • Maciej SzymkowskiEmail author
  • Emil Saeed
  • Khalid Saeed
  • Zofia Mariak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11542)

Abstract

Hard exudates are usually seen in the course of diabetic retinopathy. This illness is one of the most common reasons for blind registration in the world. Due to the data presented by World Health Organization (WHO) the number of people who lose sight because of undetected diabetes will be doubled by 2050. The purpose of this paper is to introduce an enhanced algorithm for hard exudates detection in Optical Coherence Tomography images. In these samples, dangerous pathological changes can be observed in the form of yellow-red spots. During the experiments more than 150 images were used to calculate the accuracy of the proposed approach. We created an algorithm that was implemented in development environment with Java Programming Language and Maven Framework. Classification was done on the basis of authors’ own algorithm results and compared with ophthalmologist decision. The experiments have shown the proposed approach has 97% of accuracy in hard exudates detection.

Keywords

Hard exudates Spectral-Domain OCT (Optical coherence Tomography) Retina Automated analysis Pattern recognition 

Notes

Acknowledgments

This work was supported by grant S/WI/3/2018 from Białystok University of Technology and funded with resources for research by the Ministry of Science and Higher Education in Poland.

References

  1. 1.
    James, B., Chew, C., Bron, A.: Lecture Notes. Ophthalmology, pp. 172–173 (2007)Google Scholar
  2. 2.
    Klein, B.E.: Overview of epidemiologic studies of diabetic retinopathy. Ophthalmic Epidemiol. 14(4), 179–183 (2007)CrossRefGoogle Scholar
  3. 3.
    Szymkowski, M., Saeed, E., Saeed, K.: Retina tomography and optical coherence tomography in eye diagnostic system. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds.) Advanced Computing and Systems for Security. AISC, vol. 666, pp. 31–42. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-10-8180-4_3CrossRefGoogle Scholar
  4. 4.
    Davoudi, S., et al.: Optical coherence tomography characteristics of macular edema and hard exudates and their association with lipid serum level in type 2 diabetes. Retina 36(9), 1622–1629 (2018)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Tuncay, T., Eyup, D.: Chorioretinal folds associated with different etiologies. Biomed. J. Sci. Tech. Res. 2(4) (2018)Google Scholar
  6. 6.
    Sasaki, M., Kawasaki, R., Noonan, J.E., Wong, T.Y., Lamoureux, E., Wang, J.J.: Quantitative measurement of hard exudates in patients with diabetes and their associations with serum lipid levels. Invest. Ophthalmol. Vis. Sci. 54(8), 5544–5550 (2013)CrossRefGoogle Scholar
  7. 7.
    Raman, R., Nittala, M.G., Gella, L., Pal, S.S., Sharma, T.: Retinal sensitivity over hard exudates in diabetic retinopathy. J. Ophthalmic Vis. Res. 10(2), 160–164 (2015)CrossRefGoogle Scholar
  8. 8.
    Szymkowski, M., Saeed, E.: A novel approach of retinal disorder diagnosing using optical coherence tomography scanners. In: Gavrilova, M.L., Tan, C.J.K., Chaki, N., Saeed, K. (eds.) Transactions on Computational Science XXXI. LNCS, vol. 10730, pp. 31–40. Springer, Heidelberg (2018).  https://doi.org/10.1007/978-3-662-56499-8_3CrossRefGoogle Scholar
  9. 9.
    Anitha, G.J., Maria, K.G.: Detecting hard exudates in retinal fundus images using convolutional neural network. In: Proceedings of International Conference on Current Trends Towards Converging Technologies (ICCTCT) (2018).  https://doi.org/10.1109/icctct.2018.8551079
  10. 10.
    Bharkad, S.: Morphological and neural network based approach for detection of exudates in fundus images. In: Second International Conference on Computing Methodologies and Communication (ICCMC) (2018).  https://doi.org/10.1109/iccmc.2018.8487517
  11. 11.
    Avula, B., Chakraborty, C.: Detection of hard exudates in retinal fundus images using deep learning. In: 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR) Proceedings (2018).  https://doi.org/10.1109/iciev.2018.8641016
  12. 12.
    Long, S., Huang, X., Chen, Z., Pardhan, S., Zheng, D.: Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation. BioMed. Res. Int. 2019(6a), 1–13 (2019)CrossRefGoogle Scholar
  13. 13.
    Saxena, L.P.: Niblack’s binarization method and its modifications to real-time applications: a review. Artif. Intell. Rev. 1–33 (2017)Google Scholar
  14. 14.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  15. 15.
    Eyupoglu, C.: Implementation of Bernsen’s locally adaptive binarization method for gray scale images. In: 7th International Science and Technology Conference (ISTEC), Vienna, Austria, Proceedings (2016)Google Scholar
  16. 16.
    Rokade, P., Manza, R.: Automatic detection of hard exudates in retinal images using haar wavelet transform. Int. J. Appl. Innov. Eng. Manag. 4(5), 402–410 (2015)Google Scholar
  17. 17.
    Joshi, S., Karlue, P.T.: Detection of hard exudates based on morphological feature extraction. Biomed. Pharmacol. J. 11(1), 215–225 (2018)CrossRefGoogle Scholar
  18. 18.
    Deep Learning for Hard Exudates Detection (2018). https://arxiv.org/ftp/arxiv/papers/1808/1808.03656.pdf. Accessed 21 Nov 2018
  19. 19.
    Kekre, H., Sarode, T., Parkar, T.: Hybrid approach for detection of hard exudates. Int. J. Adv. Comput. Sci. Appl. 4(1) (2013)Google Scholar
  20. 20.
    Eadgahi, M.G.F., Pourreza, H.: Localization of hard exudates in retinal fundus image by mathematical morphology operations. J. Theor. Phys. Cryptogr. 1(2) (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maciej Szymkowski
    • 1
    Email author
  • Emil Saeed
    • 2
  • Khalid Saeed
    • 1
  • Zofia Mariak
    • 2
  1. 1.Faculty of Computer ScienceBiałystok University of TechnologyBiałystokPoland
  2. 2.Faculty of Medicine, Department of OphthalmologyMedical University of BiałystokBiałystokPoland

Personalised recommendations