Glaucoma is a non-curable eye disease hence early detection is required to prevent the further progression of it. This disease leads to total blindness. To deal with the disease various automated systems have been designed. In this paper we are giving explanation for Cup to Disc ratio (CDR) measurement using fuzzy algorithm. The Cup to Disc ratio (CDR) is one of the important factor in glaucoma detection. In the proposed method, the optic disc and optic cup is segmented using fuzzy c-means clustering and thresholding. The Cup to Disc Ratio (CDR) of the color retinal fundus camera image is the primary identifier to confirm glaucoma for a patient. It classifies the given input image as normal or diseased and if it is recognized as diseased then classify the stage of glaucoma affected patient whether Moderate, Severe or normal based on the CDR ratio. In this paper we are giving a number of screenshots which shows results of different image processing techniques applied on input images.

Fundus image, Optic disc, Optic cup, Cup-to-disc ratio, Fuzzy c-means clustering

[1] S. Kavitha,, S. Karthikeyan &, K. Duraiswamy “Early detection of glaucoma in retinal images using cup to disc ratio.” In the proceedings of International Conference on Computing Communication and Networking Technologies (ICCCNT), India, pp. 1-5, 2010.
[2] J. Cheng,, J. Liu,, Y. Xu, , F. Yin, D. W. K. Wong,, N. M. Tan &, T. Y. Wong “Super pixel classification based optic disc and optic cup segmentation for glaucoma screening.” In the IEEE transactions on Medical Imaging, Vol: 32, Issue: 6, pp:1019-1032, 2013.
[3] N. M. Noor,, N. E. A. Khalid & N. M. Ariff “Optic cup and disc color channel multi-thresholding segmentation”. In the proceedings of International Conference on Control System, Computing and Engineering, pp. 530-534., 2013.
[4] C. Burana-Anusorn, W. Kongprawechnon, T. Kondo, S. Sintuwong & K. Tungpimolrut. “Image processing techniques for glaucoma detection using the cup-to-disc ratio.” Thammasat International Journal of Science and Technology, Vol. 18, Issue. 1, 2013.
[5] Hafsah Ahmad, Abubakar Yamin, Aqsa Shakeel, Syed Omer Gillani, Umer Ansari, “Detection of glaucoma using Retinal Fundus images”, in the proceedings of International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE) Islamabad, 2014
[6] M. Kankanala, & S. Kubakaddi, “Automatic segmentation of optic disc using modified multi-level thresholding.”, in the proceedings of IEEE International Symposium Signal Processing and Information Technology (ISSPIT), pp: 000125-000130, 2014.
[7] F. Khan, S. A. Khan, U. U. Yasin, I. ul Haq & U. Qamar, “Detection of glaucoma using retinal fundus images.” Biomedical Engineering International Conference (BMEiCON), 6th, pp. 1-5, 2013
[8] A. Yadav , V.K. Harit “Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique”. International Journal of Scientific Research in Computer Science and Engineering Vol.4, Issue.6, pp.1-7, 2016.
[9] D. Dey “Image Encryption using Linear Cryptanalysis and different Fuzzy operations” , International Journal of Scientific Research in Computer Science and Engineering Vol. 6, Issue. 4, pp: 01-08, 2018
[10] N. E. A.Khalid, , N. M. Noor, & N. M. Ariff. “Fuzzy c-means (FCM) for optic cup and disc segmentation with morphological operation.”, Procedia Computer Science, 42, pp: 255-262, 2014.