EARLY DETECTION OF GLAUCOMA USING EMPIRICAL WAVELET TRANSFORM

Authors

  • Miss.Sheetal G Dulange Dept. of Electronics and Telecommunication N.B.N. Sinhgad College of Engineering, Solapur, India.
  • Prof. S.S.Shirgan Dept. of Electronics and Telecommunication, Sinhgad College of Engineering, Solapur ,India

Keywords:

Glaucoma, Empirical wavelet Transform, Feature selection, Least Support Vector Machine, Structural features

Abstract

Glaucoma is second leading ocular disease and early detection of Glaucoma can prevent progression
progression of disease and consequently loss of vision. Unfortunately, Glaucoma symptoms are painless the brain
compensates gradual vision impairment to considerable extent. Here we presents new methodology for an automated
diagnosis of glaucoma using digital Fundus image based on Empirical Wavelet Transform(EWT).EWT is used to
decompose image and structural features are extracted from decomposed EWT components. These extracted features are
ranked based on t value feature selection algorithm. These features are used for classification of normal and glaucoma
image using Least Square Support Vector Machine Classifier.

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Published

2018-01-25

How to Cite

Miss.Sheetal G Dulange, & Prof. S.S.Shirgan. (2018). EARLY DETECTION OF GLAUCOMA USING EMPIRICAL WAVELET TRANSFORM. International Journal of Advance Engineering and Research Development (IJAERD), 5(1), 678–683. Retrieved from https://ijaerd.org/index.php/IJAERD/article/view/2154

Issue

Vol. 5 No. 1 (2018): Volume 5 Issue 1 January 2018

Section

Articles