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Hybrid Feature Extraction in Retinal Fundus Images used in Glaucoma Detection


Shah Nidhi R. , L.J. Institute of Engineering and Technology


Glaucoma, Hybrid Feature Extraction, Retinal Fundus Images, SVM Classifiers


Glaucoma is caused due to unawareness in people which can be resulted in to the blindness. For patients affected by this, mass screening can be the best curable solution which can help to extend symptom-free life. Using hybrid feature extraction from digital fundus images, we propose a novel low cost automated glaucoma diagnosis system. Higher order spectra (HOS) and discrete wavelet transform (DWT) features are used for automated identification of normal glaucoma classes. Vector machine (SVM) classifier with linear, radial basis function (RBF) and polynomial order 1, 2, 3 are the extracted features fed to support in order to select the best kernel for automated decision making. Our results shows that the combination of HOS and DWT features in feature extraction performs better than the other feature extraction and correctly identifies the glaucoma images with an accuracy of more than 92.48%. System proposed by us is having clinically significance features and this system can be adopted for detecting glaucoma with accurate results.

Other Details

Paper ID: IJSRDV3I40702
Published in: Volume : 3, Issue : 4
Publication Date: 01/07/2015
Page(s): 1096-1099

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