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Abstract #67206 Published in IGR 17-4
Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted from Fundus Images
Maheshwari S; Pachori RB; Acharya URIEEE journal of biomedical and health informatics 2016; 0:
Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve subsequently causes loss of vision. The available scanning methods are Heidelberg Retinal Tomography (HRT), Scanning Laser Polarimetry (SLP) and Optical Coherence Tomography (OCT). These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on Empirical Wavelet Transform (EWT). The EWT is used to decompose the image and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using Least Squares Support Vector Machine (LS-SVM) classifier. The LSSVM is employed for classification with Radial Basis Function (RBF), Morlet wavelet and Mexican-hat wavelet kernels. The classification accuracy of proposed method is 98.33% and 96.67% using three-fold and ten-fold cross validation respectively.
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Classification:
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.8.2 Posterior segment (Part of: 6 Clinical examination methods > 6.8 Photography)
