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DC-Gnet for detection of glaucoma in retinal fundus imaging

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Abstract

Glaucoma is a retinal disease caused due to increased intraocular pressure in the eyes. It is the second most dominant cause of irreversible blindness after cataract, and if this remains undiagnosed, it may become the first common cause. Ophthalmologists use different comprehensive retinal examinations such as ophthalmoscopy, tonometry, perimetry, gonioscopy and pachymetry to diagnose glaucoma. But all these approaches are manual and time-consuming. Thus, a computer-aided diagnosis system may aid as an assistive measure for the initial screening of glaucoma for diagnosis purposes, thereby reducing the computational complexity. This paper presents a deep learning-based disc cup segmentation glaucoma network (DC-Gnet) for the extraction of structural features namely cup-to-disc ratio, disc damage likelihood scale and inferior superior nasal temporal regions for diagnosis of glaucoma. The proposed approach of segmentation has been tested on RIM-One and Drishti-GS dataset. Further, based on experimental analysis, the DC-Gnet is found to outperform U-net, Gnet and Deep-lab architectures.

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References

  1. Foster, P.J., Buhrmann, R., Quigley, H.A., Johnson, G.J.: The definition and classification of glaucoma in prevalence surveys. Br. J. Ophthalmol. 86, 238–242 (2002)

    Article  Google Scholar 

  2. Saxena, R., Singh, D., Vashist, P.: Glaucoma: an emerging peril. Indian J. Community Med. 38, 135–137 (2013)

    Article  Google Scholar 

  3. Glaucoma Research foundation: Five Common Glaucoma Tests. https://www.glaucoma.org/glaucoma/diagnostic-tests.php. (Available 4 April 2019). (Accessed 10 May 2019)

  4. Yamada, S., Komatsu, K., Ema, T., Inventors; Toshiba Corp, Assignee: Computer-aided diagnosis system for medical use. United States Patent US 5,235,510 (1993)

  5. Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)

    Article  Google Scholar 

  6. Garway-Heath, D.F., Ruben, S.T., Viswanathan, A., Hitchings, R.A.: Vertical cup/disc ratio in relation to optic disc size: its value in the assessment of the glaucoma suspect. Br. J. Ophthalmol. 82(10), 1118–1124 (1998)

    Article  Google Scholar 

  7. Harizman, N., Oliveira, C., Chiang, A., Tello, C., Marmor, M., Ritch, R., Liebmann, J.M.: The ISNT rule and differentiation of normal from glaucomatous eyes. Arch. Ophthalmol. 124(11), 1579–1583 (2006)

    Article  Google Scholar 

  8. Henderer, J.D., Liu, C., Kesen, M., Altangerel, U., Bayer, A., Steinmann, W.C., Spaeth, G.L.: Reliability of the disk damage likelihood scale. Am. J. Ophthalmol. 135(1), 44–48 (2003)

    Article  Google Scholar 

  9. Khalid, N.E.A., Noor, N.M., Ariff, N.M.: Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation. Procedia Comput. Sci. 42, 255–262 (2014)

    Article  Google Scholar 

  10. Thakur, N., Juneja, M.: Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomed. Signal Process. Control 1(42), 162–189 (2018)

    Article  Google Scholar 

  11. Gentle Dive into Math Behind Convolutional Neural Networks: https://towardsdatascience.com/gentle-dive-into-math-behind-convolutional-neural-networks-79a07dd44cf9. (Available 13 April 2019). (Accessed 17 July 2019)

  12. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)

  13. Walter, T., Klein, J.C.: Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. In: Springer Proceedings of International Symposium on Medical Data Analysis, pp. 282–287 (2015)

  14. Pallawala, P.M.D.S., Hsu, W., Lee, M.L., Eong, K.G.A.: Automated optic disc localization and contour detection using ellipse fitting and wavelet transform. In: Springer Proceedings of European Conference on Computer Vision, pp. 139–151 (2004)

  15. Abdel-Ghafar, R.A., Morris, T.: Progress towards automated detection and characterization of the optic disc in glaucoma and diabetic retinopathy. Med. Inform. Internet Med. 32(1), 19–25 (2007)

    Article  Google Scholar 

  16. Liu, J., Wong, D.W.K., Lim, J.H., Jia, X., Yin, F., Li, H., Xiong, W., Wong, T.Y.: Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio. In: IEEE Proceedings of 3rd Conference on Industrial Electronics and Applications, pp. 1828–1832 (2008)

  17. Zhang, Z., Liu, J., Wong, W.K., Tan, N.M., Lim, J.H., Lu, S., Li, H., Liang, Z., Wong, T.Y.: Neuro-retinal optic cup detection in glaucoma diagnosis. In: IEEE Proceedings of 2nd International Conference on Biomedical Engineering and Informatics, pp. 1–4 (2009)

  18. Sarkar, D., Das, S.: Automated glaucoma detection of medical image using biogeography based optimization. In: Proceedings of Advances in Optical Science and Engineering, pp. 381–388. Springer, Singapore (2017)

  19. Priyadharsini, R., Beulah, A., Sharmila, T.S.: Optic disc and cup segmentation in fundus retinal images using feature detection and morphological techniques. Curr. Sci. 115(4), 748 (2018)

    Article  Google Scholar 

  20. Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Comparison of colour spaces for optic disc localisation in retinal images. In: Proceedings of 16th International Conference on Pattern Recognition, pp. 743–746 (2002)

  21. Chrastek, R., Wolf, M., Donath, K., Niemann, H., Paulus, D., Hothorn, T., Lausen, B., Lämmer, R., Mardin, C.Y., Michelson, G.: Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med. Image Anal. 9(4), 297–314 (2005)

    Article  Google Scholar 

  22. Blanco, M., Penedo, M.G., Barreira, N., Penas, M., Carreira, M.J.: Localization and extraction of the optic disc using the fuzzy circular Hough transform. In: Proceedings of International Conference on Artificial Intelligence and Soft Computing, pp. 712–721 (2006)

  23. Datt, J.G., Sivaswamy, J., Krishnadas, S.R.: Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans. Med. Imaging 30(6), 1192–1205 (2011)

    Article  Google Scholar 

  24. Yin, F., Liu, J., Wong, D.W.K., Tan, N.M., Cheung, C., Baskaran, M., Aung, T., Wong, T.Y.: Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis. In: Proceedings of 25th International Symposium on Computer-Based Medical Systems, pp. 1–6 (2012)

  25. Xu, Y., Lin, S., Wong, D.W., Liu, J., Xu, D.: Efficient reconstruction-based optic cup localization for glaucoma screening. In: Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 445–452 (2013)

  26. Cheng, J., Yin, F., Wong, D.W., Tao, D., Liu, J.: Sparse dissimilarity-constrained coding for glaucoma screening. IEEE Trans. Biomed. Eng. 62(5), 1395–1403 (2015)

    Article  Google Scholar 

  27. Kavitha, S., Karthikeyan, S., Duraiswamy, K.: Early detection of glaucoma in retinal images using cup to disc ratio. In: Proceedings of International Conference on Computing Communication and Networking Technologies, pp. 1–5 (2010)

  28. Mittapalli, P.S., Kande, G.B.: Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomed. Signal Process. Control 24, 34–46 (2016)

    Article  Google Scholar 

  29. Sevastopolsky, A.: Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognit. Image Anal. 27(3), 618–624 (2017)

    Article  Google Scholar 

  30. Fu, H., Cheng, J., Xu, Y., Wong, D.W., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018)

    Article  Google Scholar 

  31. Sun, X., Xu, Y., Zhao, W., You, T., Liu, J.: Optic disc segmentation from retinal fundus images via deep object detection networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5954–5957. IEEE (2018)

  32. Chakravarty, A., Sivswamy, J.: A deep learning based joint segmentation and classification framework for glaucoma assesment in retinal color fundus images. arXiv preprint arXiv:1808.01355 (2018)

  33. Edupuganti, V.G., Chawla, A., Kale, A.: Automatic optic disk and cup segmentation of fundus images using deep learning. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2227–2231. IEEE (2018)

  34. Wang, L., Liu, H., Zhang, J., Chen, H., Pu, J.: Automated segmentation of the optic disc using the deep learning. In: Medical Imaging 2019: Image Processing, vol. 10949, p. 1094923. International Society for Optics and Photonics (2019)

  35. Juneja, M., Singh, S., Agarwal, N., Bali, S., Gupta, S., Thakur, N., Jindal, P.: Automated detection of glaucoma using deep learning convolution network (G-net). Multimed. Tools Appl. (2019). https://doi.org/10.1007/s11042-019-7460-4

    Article  Google Scholar 

  36. Kang, H., Wang, K., Guo, S., Gao, Y., Li, N., Weng, J., Li, X., Li, T.: Pixel quantification for robust segmentation of optic cup (2019). https://grand-challenge-public.s3.amazonaws.com/f/challenge/229/b14c03ff-a260-4272-b5ee-67e4b695f14a/REFUGE-NKSG-slides.pdf

  37. Sivaswamy, J., Krishnadas, S.R., Joshi, G.D., Jain, M., Tabish, A.U.: Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 53–56. IEEE (2014)

  38. Fumero, F., Alayón, S., Sanchez, J.L., Sigut, J., Gonzalez-Hernandez, M.: RIM-One: an open retinal image database for optic nerve evaluation. In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6. IEEE (2011)

  39. Jackson, D.A., Somers, K.M., Harvey, H.H.: Similarity coefficients: measures of co-occurrence and association or simply measures of occurrence. Am. Nat. 133(3), 436–453 (1989)

    Article  Google Scholar 

  40. Ferdous, R.: An efficient k-means algorithm integrated with Jaccard distance measure for document clustering. In: 2009 First Asian Himalayas International Conference on Internet, pp. 1–6. IEEE (2009)

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Acknowledgements

The authors are also grateful to the Ministry of Human Resource Development (MHRD), Govt. of India for funding this Project (17-11/2015-PN-1) under Design Innovation Centre (DIC) sub-theme Medical Devices & Restorative Technologies.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Mamta Juneja, Sarthak Thakur, Anuj Wani, Archit Uniyal, Niharika Thakur & Prashant Jindal

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  1. Mamta Juneja
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Correspondence to Prashant Jindal.

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Juneja, M., Thakur, S., Wani, A. et al. DC-Gnet for detection of glaucoma in retinal fundus imaging. Machine Vision and Applications 31, 34 (2020). https://doi.org/10.1007/s00138-020-01085-2

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