Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India

Research Article

Diagnosing the level of Glaucoma from Fundus Image Using Empirical Wavelet Transform

  • @INPROCEEDINGS{10.4108/eai.16-5-2020.2304035,
        author={R Narayana Swamy and Syed Thouheed  Ahmed and K  Thanuja and S  Ashwini and Syeda Ayesha Siddiqha and Afifa Salsabil Fathima},
        title={Diagnosing the level of Glaucoma from Fundus Image Using Empirical Wavelet Transform},
        proceedings={Proceedings of the First  International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India},
        publisher={EAI},
        proceedings_a={ICASISET},
        year={2021},
        month={1},
        keywords={empirical wavelet transform (ewt) glaucoma detection image processing},
        doi={10.4108/eai.16-5-2020.2304035}
    }
    
  • R Narayana Swamy
    Syed Thouheed Ahmed
    K Thanuja
    S Ashwini
    Syeda Ayesha Siddiqha
    Afifa Salsabil Fathima
    Year: 2021
    Diagnosing the level of Glaucoma from Fundus Image Using Empirical Wavelet Transform
    ICASISET
    EAI
    DOI: 10.4108/eai.16-5-2020.2304035
R Narayana Swamy1,*, Syed Thouheed Ahmed2, K Thanuja3, S Ashwini4, Syeda Ayesha Siddiqha4, Afifa Salsabil Fathima4
  • 1: JAIN University, Bengaluru
  • 2: Cambridge Institute of Technology, Bengaluru
  • 3: REVA University, Bengaluru
  • 4: Bengaluru
*Contact email: test@test.test

Abstract

An increased pressure of fluid in optic nerve can subsequently leads to permanent blindness are known as Glaucoma. The normal pressure of eye is 15mmHg or even lower, once it is higher than 30mmHg then there is risk in vi-sion loss. There are many existing technique that require experienced clinicians and cost effective. These systems use higher order spectra and discrete wavelet transform features for extracting the values and fed to classifier for normaliza-tion and ranking the feature. In this paper presenting a new methodology for di-agnosis of glaucoma based on EWT. Empirical wavelet transform is applied on image to format the sub band which is also called as decomposed image. These features are sustained into neural network system that produces ne value from n iteration and classify images into mild, intermediate and heavily affected eye using Fundus images.