Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image

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Published Sep 30, 2020
DOI https://doi.org/10.25077/jitce.4.02.105-109.2020

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Pulung Hendro Prastyo
Universitas Gadjah Mada
Amin Siddiq Sumi Annis Nuraini

Abstract

Retinal fundus images are used by ophthalmologists to diagnose eye disease, such as glaucoma disease. The diagnosis of glaucoma is done by measuring changes in the cup-to-disc ratio. Segmenting the optic cup helps petrify ophthalmologists calculate the CDR of the retinal fundus image. This study proposed a deep learning approach using U-Net architecture to carry out segmentation task. This proposed method was evaluated on 650 color retinal fundus image. Then, U-Net was configured using 160 epochs, image input size = 128x128, Batch size = 32, optimizer = Adam, and loss function = Binary Cross Entropy. We employed the Dice Coefficient as the evaluator. Besides, the segmentation results were compared to the ground truth images. According to the experimental results, the performance of optic cup segmentation achieved 98.42% for the Dice coefficient and loss of 1,58%. These results implied that our proposed method succeeded in segmenting the optic cup on color retinal fundus images.

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How to Cite
Prastyo, P., Sumi, A., & Nuraini, A. (2020, September 30). Optic Cup Segmentation using U-Net Architecture on Retinal Fundus Image. JITCE (Journal of Information Technology and Computer Engineering), 4(02), 105-109. https://doi.org/https://doi.org/10.25077/jitce.4.02.105-109.2020
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Vol 4 No 02 (2020): Journal of Information Technology and Computer Engineering
Section
Articles

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References

[1] Z. Zhang et al., “ORIGA-light: An online retinal fundus image database for glaucoma analysis and research,” in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010, pp. 3065–3068, doi: 10.1109/IEMBS.2010.5626137.
[2] Y. Jiang, N. Tan, and T. Peng, “Optic Disc and Cup Segmentation Based on Deep Convolutional Generative Adversarial Networks,” IEEE Access, vol. 7, pp. 64483–64493, 2019, doi: 10.1109/ACCESS.2019.2917508.
[3] G. D. Joshi, J. Sivaswamy, and S. R. Krishnadas, “Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment,” IEEE Trans. Med. Imaging, vol. 30, no. 6, pp. 1192–1205, 2011, doi: 10.1109/TMI.2011.2106509.
[4] H. A. Nugroho, W. K. . Oktoeberza, A. Erasari, A. Utami, and C. Cahyono, “Segmentation of Optic Disc and Optic Cup in Colour Fundus Images Based on Morphological Reconstruction,” in 9th International Conference on Information Technology and Electrical Engineering (ICITEE), 2017, pp. 1–5, doi: 10.1017/CBO9781107415324.004.
[5] P. Qin, L. Wang, and H. Lv, “Optic disc and cup segmentation based on deep learning,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, 2019, pp. 1835–1840, doi: 10.1109/ITNEC.2019.8729455.
[6] A. Almazroa, S. Alodhayb, R. Burman, W. Sun, K. Raahemifar, and V. Lakshminarayanan, “Optic cup segmentation based on extracting blood vessel kinks and cup thresholding using Type-II fuzzy approach,” in 2nd International Conference on Opto-Electronics and Applied Optics: Advances in Optical Sciences and Engineering II, IEM OPTRONIX 2015, 2015, pp. 2–6, doi: 10.1109/OPTRONIX.2015.7345519.
[7] D. W. K. Wong, J. Liu, J. H. Lim, H. Li, and T. Y. Wong, “Automated detection of kinks from blood vessels for optic cup segmentation in retinal images,” in Medical Imaging 2009: Computer-Aided Diagnosis, 2009, vol. 7260, pp. 459–466, doi: 10.1117/12.810784.
[8] R. Ingle and P. Mishra, “Cup Segmentation by Gradient Method for the Assessment of Glaucoma from Retinal Image,” Int. J. Eng. Trends Technol., vol. 4, no. 6, pp. 2540–2543, 2013.
[9] M. Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari, “Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation,” CoRR, vol. abs/1802.06955, 2018, [Online]. Available: http://arxiv.org/abs/1802.06955.
[10] A. O. Joshua, F. V. Nelwamondo, and G. Mabuza-Hocquet, “Segmentation of Optic Cup and Disc for Diagnosis of Glaucoma on Retinal Fundus Images,” in Proceedings - 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa, SAUPEC/RobMech/PRASA 2019, 2019, pp. 183–187, doi: 10.1109/RoboMech.2019.8704727.
[11] P. Xiuqin, Q. Zhang, H. Zhang, and S. Li, “A fundus retinal vessels segmentation scheme based on the improved deep learning u-net model,” IEEE Access, vol. 7, pp. 122634–122643, 2019, doi: 10.1109/ACCESS.2019.2935138.
[12] W. Zhang, P. Tang, L. Zhao, and Q. Huang, “A comparative study of U-nets with various convolution components for building extraction,” in 2019 Joint Urban Remote Sensing Event, JURSE 2019, 2019, pp. 1–4, doi: 10.1109/JURSE.2019.8809055.
[13] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015, 2015, pp. 234–241.