Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images

Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. These methods a...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2017-05, Vol.21 (3), p.803-813
Main Authors: Maheshwari, Shishir, Pachori, Ram Bilas, Acharya, U. Rajendra
Format: Article
Language:English
Subjects:
Algorithms
Automation
Biomedical optical imaging
Classification
Correntropy
Diagnostic Techniques, Ophthalmological
empirical wavelet transform (EWT)
Feature extraction
feature selection
Fundus Oculi
Glaucoma
Glaucoma - diagnostic imaging
Humans
Image Interpretation, Computer-Assisted - methods
Kernels
Least-Squares Analysis
least-squares support vector machine (LS-SVM) classifier
Medical diagnosis
Optic nerve
Optical imaging
Reproducibility of Results
Retina
Retina - diagnostic imaging
Support vector machines
Tomography
Wavelet Analysis
Wavelet transforms
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Summary:Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve and subsequently causes loss of vision. The available scanning methods are Heidelberg retinal tomography, scanning laser polarimetry, and optical coherence tomography. 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 LS-SVM is employed for classification with radial basis function, Morlet wavelet, and Mexican-hat wavelet kernels. The classification accuracy of the proposed method is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2016.2544961