Advertisement

Image Inpainting for Hemorrhage Detection in Mass Screening of Diabetic Retinopathy

  • Anupama Awati
  • H. Chinmayee Rao
  • M. R. Patil
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Diabetic retinopathy (DR) is one of the main causes of vision loss. The severity of DR can be analyzed using human retinal images (fundus image). Affected fundus image consists of hemorrhages, microaneurysms, and exudates along with blood vessels. In order to accurately detect the level of severity of the disease, the blood vessels are inpainted using fast marching method (FMM). The technique implemented in this paper involves image enhancement using green channel image and histogram equalization followed by mask generation and inpainting. The severity of the disease can be categorized accurately by inpainting the blood vessels using FMM. The proposed technique is tested using standard test databases HRF and DRIVE. The algorithm can be effectively used for mass screening of DR. This technique is a fundamental step in designing computer-aided diagnosis system for ophthalmic disorders.

Keywords

Retinopathy Image inpainting Fundus image Mass screening 

References

  1. 1.
    Bugeau, A., Bertalmío, M., Caselles, V., Sapiro, G.: A comprehensive framework for image inpainting. IEEE Trans. Image Process. 19(10) (2010)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Zhang, D., Li, X., Shang, X., Yi, Y., Wang, Y.: Stand, robust hemorrhage detection in diabetic retinopathy image. IEEE Trans. Image Process. (2011). 978-1-4577-0121-4/11/Google Scholar
  3. 3.
    Biran, A., Bidari, P.S., Raahemifar, K.: Automatic method for exudates and hemorrhages detection from fundus retinal images. World Acad. Sci. Eng. Technol. Int. Eng. 10(9) (2016)Google Scholar
  4. 4.
    Colomera, A., Naranjoa, V., Engan, K., Skretting, K.: Assessment of sparse-based inpainting for retinal vessel removal. Signal Process. Image Commun. Sci. Direct (2017)Google Scholar
  5. 5.
    Zhang, X., Thibault, G., Decencière, E., Marcotegui, B., Laÿ, B., Danno, R., Cazuguel, G., Quellec, G., Lamard, M., Massin, P., Chabouis, A., Victor, Z., Erginay, A.: Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med. Image Anal. (2014)Google Scholar
  6. 6.
    Kaur, N., Kaur, J., Acharyya, M., Kapoor, N., Chatterjee, S., Gupta, S.: A supervised approach for automated detection of hemorrhages in retinal fundus images. In: IEEE Trans. (2016)Google Scholar
  7. 7.
    Zhou, W., Wu, C., Yi, Y., Du, W.: Automatic detection of exudates in digital color fundus images using super pixel multi-feature classification. IEEE Trans. 2169–3536 (2017)Google Scholar
  8. 8.
    Pavani, T.C., Pardhasaradhi, M.: Efficient localization of blood vessel in painting based technique and segmentation of optic disc in digital fundus images. Int. J. Adv. Technol. Innov. Res. 08(01), pp. 0111–0116, Jan 2016Google Scholar
  9. 9.
    Biradar, R.L., Kohir, V.V.: A novel image inpainting technique based on median diffusion. S¯adhan¯ a, vol. 38, Part 4, pp. 621–644. c Indian Academy of Sciences, Aug 2013MathSciNetCrossRefGoogle Scholar
  10. 10.
    Telea, A.: An Image Inpainting Technique Based on the Fast Marching Method (2004)CrossRefGoogle Scholar
  11. 11.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Heckbert, P. (ed.) Graphics Gems IV. Academic Press (1994)Google Scholar
  12. 12.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Akeley, Kurt (ed.) Proceedings SIGGRAPH 2000, Computer Graphics Proceedings, Annual Conference Series, pp. 417–424. Addison-Wesley, Reading, MA (2000)Google Scholar
  13. 13.
    Budai, A., Bock, R., Maier, A., Hornegger, J., Mishelson, G.: Robust vessel segmentation in fundud images. Int. J. Biomed. Imaging 2013 (2013)CrossRefGoogle Scholar
  14. 14.
    Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)CrossRefGoogle Scholar
  15. 15.
    Sreejini, K.S., Govindan, V.K.: A review of computer aided detection of anatomical structures and lesions of DR from color retina images. Int. J. Images Graph. Signal Process. (2015)Google Scholar
  16. 16.
    De, C., Shih, F.Y.: Improved Image Inpainting using Maximum Value Edge Detector. IEEE (2012). 978-1-4673-1830-3/12/$31.00 ©2012Google Scholar
  17. 17.
    Noori, H., Saryazdi, S., Nezamabadi-pour, H.: A convolution based image inpainting. In: International Conference on Communication Engineering, University of Sistan and Baluchestan, pp. 22–24 (2010)Google Scholar
  18. 18.
    Bertalmio, M., et al.: Image inpainting. In: Proceeding of SIGGRAPH 2000 Computer Graphics Processings, pp. 417–424Google Scholar
  19. 19.
    Hadhoud, M.M., Moustafa, K.A., Shenoda, S.Z.: Digital images inpainting using modified convolution based method. Int. J. Signal Process. Image Process. Pattern Recogn. 1–10Google Scholar
  20. 20.
    Elango, P., Murugesan, K.: Digital image inpainting using cellular neural network. Int. J. Open Probl. Comput. Sci. Math. 2(3), 439–449 (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Anupama Awati
    • 1
  • H. Chinmayee Rao
    • 1
  • M. R. Patil
    • 2
  1. 1.Department of Electronics and CommunicationKLS Gogte Institute of TechnologyBelagaviIndia
  2. 2.Department of Electronics and CommunicationJAGMITJamakhandiIndia

Personalised recommendations