Glaucoma detection using cup to disc ratio and artificial neural networks

  • Authors

    • Gayathri R.
    • Rao P. V.
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.5.9135
  • Pre-Processing, Cup-to-Disc, Glaucoma, Morphological operations, Segmentation, Artificial Neural Networks.

  • Now-a-days, the most commonly predicted eye disease in human beings is glaucoma; loss of vision gradually may turn into blindness. Advanced image handling methods empower osteopathic specialist to distinguish and treat a few eye infections like diabetic retinopathy and glaucoma. The pressure in the optic nerve of the eye may lead to get affected by glaucoma, which is most regular reason for visual deficiency of the peoples, if not treated appropriately at early stage. The main objective of this paper is the detection of glaucoma and classifies the disease based on its severity using artificial neural network. In this paper mainly focused on pre -processing of retinal fundus images for improving the quality of detection and easy to further handling. The simulation results to obtain using MATLAB for the better accuracy in detecting glaucoma for abnormality using Cup to Disc ratio of retinal fund us images. 

  • References

    1. [1] N. Annu ,& J. Justin , (2013) “Classification of Glaucoma Images using Wavelet based Energy Features and PCAâ€, International Journal of Scientific & Engineering Research, Vol.5, No.2, ISSN: 0975-4024, 2013.

      [2] U. R. Acharya, S. Dua , S.VinithaSree& Chua C.K. Chua, “Automated diagnosis of glaucoma using texture and higher order spectra featuresâ€, Information Technology in Biomedicine, IEEE Transactions on,15(3), 449-455, 2011.

      [3]D. Chaitali, S.B Dhumane& P.G. Patil “Automated Glaucoma Detection using Cup to Disc Ratioâ€, Vol. 4, Issue 7, ISSN(Online) : 2319-875, ISSN (Print) : 2347-6710, 2015.

      [4] S. Dua , U.R. Acharya , P. Chowriappa , &S.V.Sree , “Wavelet-based energy features for glaucomatous image classificationâ€, Information Technology in Biomedicine, IEEE Transactions on, 16(1), pp 80-87,2012.

      [5] R. Gayathri,, P.V. Rao, & S. Aruna, “Automated glaucoma detection system based on wavelet energy features and ANNâ€, International Conference in Advances in Computing, Communications and Informatics ( ICACCI), New Delhi, pp. 2808-2812, IEEE, 2014

      [6] R. Gayathri , S. Aruna , P.V. Rao , (2016) “A Novel Approach For Glaucoma Detection Using Cup To Disk Ratioâ€, International Conference of Researches in Science, Management and Engineering , Journals of applied sciences and

      [7] D.S. Grewal , R. Jain , P.S Grewal. &Rihani ,KurnikaChoudhary, ShamikTiwari , “ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rimâ€, International Journal of Computer Applications (0975 – 8887) Volume 111 – No 11,2015.

      [9] C. H. Li, B.C Kuo , C.T. Lin & C. Huang , “A spatial–contextual support vector machine for remotely sensed image classificationâ€, Geoscience and Remote Sensing, IEEE Transactions on, 50(3), 784-799, 2012

      [10] F. Mianji& Y. Zhang , (2011) “SVM-based unmixing- to- classification conversion for hyper spectral abundance quantification. Geoscience and Remote Sensingâ€, IEEE Transactions on, 49(11), 4318-4327, 2011.

      [11] Nidhi Shah, NarendraLimbad, “A Literature Survey on Glaucoma Detection Techniques using Fundus Imagesâ€, IJSRD - Vol. 2, Issue 09, ISSN (online): 2321-0613, 2014.

      [12] Parveen Kumar, Pooja Sharma, “Artificial Neural Networks-A Studyâ€, International Journal of Emerging Engineering Research and Technology, Volume 2, Issue 2, PP 143-148, 2014.

      [13] PoojaChaudhari , A. GirishKulkarni, “Using Artificial Neural Network to Detect Glaucoma with the Help of Cup to Disk Ratioâ€, IJARECEVolume5,Issue7, page no 1967, 2016.

      [14] G. Quellec , S.R Russell, & M.D. Abrà moff , “Optimal filter framework for automated, instantaneous detection of lesions in retinal imagesâ€, Medical Imaging, IEEE Transactions on, 30(2), 523-533, 2011.

      [15] P.V. Rao ,R. Gayathri& R. Sunitha ,“A Novel Approach for Design and Analysis of Diabetic Retinopathy Glaucoma Detection using Cup to Disk Ration and ANNâ€, 2nd International Conference on Nanomaterials and Technologies(CNT),Hyderabad, pp. 2211-8128, Published by Elsevier.2014.

      [16] R. Raghul ,S. Lakshmi , “A NEW APPROACH FOR DETECTION OF BLOOD VESSEL TRACKING SYSTEM AND GLAUCOMA FOR DIABETIC PATIENTâ€, VOL. 11, NO. 11, ISSN 1819-6608, 2016.

      [17] O. Sheeba ,Jithin George, P.K. Rajin , and SherinGeorge,“Glaucoma Detection Using Artificial Neural Networkâ€, IACSIT, Vol. 6, No. 2. DOI: 10.7763/IJET.2014.V6. 687,2014.

      [18] L. Shen& S. Jia (2011), “ Three-dimensional Gabor wavelets for pixel-based hyperspectral imagery classificationâ€, IEEE Trans, on Geoscience And Remote Sensing, Vol. 49 , No 12, 5039-5046, 2011.

      [19] P.P Swapna and M.G. Mini, “A Regression Neural Network based Glaucoma Detection System using Texture Featuresâ€, IJCCIE, Vol. 3, Issue 2, ISSN 2349-1469 EISSN 2349-1477,2016.

      [20] T. Vijayan, Ashutosh Singh,“Glaucoma Recognition and Segmentation Using Feed Forward Neural Network and Optical physicsâ€, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering ,Vol. 4, Issue 4,2015.

      [21] Gayathri R, DrRao.P.V, Aruna .S, “Glaucoma Detection System Based on Wavelet Energy Features , Cup to Disc Ratio, Threshold Value And ANNâ€, Cashtech-2017 conference, VignanaBharathi Institute of Technology , page num 1277-1283, Telangana , Hyderabad, India.

      [22] T.Padmapriya, Ms. N. Dhivya, Ms U. Udhayamathi, “Minimizing Communication Cost In Wireless Sensor Networks To Avoid Packet Retransmissionâ€, International Innovative Research Journal of Engineering and Technology, Vol. 2, Special Issue, pp. 38-42.

      [23] S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015,Publisher: IEEE,DOI: 10.1109/ECS.2015.7124833.

      [24] Rajesh, M., and J. M. Gnanasekar. "Constructing Well-Organized Wireless Sensor Networks with Low-Level Identification." World Engineering & Applied Sciences Journal 7.1 (2016).

      [[25] S.V.Manikanthan and T.Padmapriya “Recent Trends In M2m Communications In 4g Networks And Evolution Towards 5gâ€, International Journal of Pure and Applied Mathematics, ISSN NO:1314-3395, Vol-115, Issue -8, Sep 2017.

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    R., G., & P. V., R. (2017). Glaucoma detection using cup to disc ratio and artificial neural networks. International Journal of Engineering & Technology, 7(1.5), 135-140. https://doi.org/10.14419/ijet.v7i1.5.9135