Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images
01 May 2017-IEEE Journal of Biomedical and Health Informatics (IEEE J Biomed Health Inform)-Vol. 21, Iss: 3, pp 803-813
TL;DR: A new methodology for an automated diagnosis of glaucoma using digital fundus images based on empirical wavelet transform (EWT) is presented and the classification accuracy is 98.33% and 96.67% using threefold and tenfold cross validation, respectively.
Abstract: 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.
Citations
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TL;DR: An eighteen layer CNN framework is proposed for glaucoma diagnosis with the highest accuracy of 98.13% using 1426 fundus images, which demonstrates the robustness of the system, which can be used as a supplementary tool for the clinicians to validate their decisions.
325 citations
TL;DR: Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings and the proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
Abstract: Objective : This paper investigates the multivariate oscillatory nature of electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure detection. Methods : The empirical wavelet transform (EWT) has been explored for the multivariate signals in order to determine the joint instantaneous amplitudes and frequencies in signal adaptive frequency scales. The proposed multivariate extension of EWT has been studied on multivariate multicomponent synthetic signal, as well as on multivariate EEG signals of Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) scalp EEG database. In a moving-window-based analysis, 2-s-duration multivariate EEG signal epochs containing five automatically selected channels have been decomposed and three features have been extracted from each 1-s part of the 2-s-duration joint instantaneous amplitudes of multivariate EEG signals. The extracted features from each oscillatory level have been processed using a proposed feature processing step and joint features have been computed in order to achieve better discrimination of seizure and seizure-free EEG signal epochs. Results : The proposed detection method has been evaluated over 177 h of EEG records using six classifiers. We have achieved average sensitivity, specificity, and accuracy values as 97.91%, 99.57%, and 99.41%, respectively, using tenfold cross-validation method, which are higher than the compared state of art methods studied on this database. Conclusion : Efficient detection of epileptic seizure is achieved when seizure events appear for long duration in hours long EEG recordings. Significance : The proposed method develops time–frequency plane for multivariate signals and builds patient-specific models for EEG seizure detection.
291 citations
Cites methods from "Automated Diagnosis of Glaucoma Usi..."
...[20] decomposed images using 2D-EWT and extracted correntropy features from 2D-EWT decomposed components to classify the normal and...
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TL;DR: A novel class of orthogonal wavelet filter banks which are localized in time–frequency domain to detect FC and NFC EEG signals automatically and help in localization of the affected brain area which needs to undergo surgery is employed.
Abstract: It is difficult to detect subtle and vital differences in electroencephalogram (EEG) signals simply by visual inspection. Further, the non-stationary nature of EEG signals makes the task more difficult. Determination of epileptic focus is essential for the treatment of pharmacoresistant focal epilepsy. This requires accurate separation of focal and non-focal groups of EEG signals. Hence, an intelligent system that can detect and discriminate focal–class (FC) and non–focal–class (NFC) of EEG signals automatically can aid the clinicians in their diagnosis. In order to facilitate accurate analysis of non-stationary signals, joint time–frequency localized bases are highly desirable. The performance of wavelet bases is found to be effective in analyzing transient and abrupt behavior of EEG signals. Hence, we employ a novel class of orthogonal wavelet filter banks which are localized in time–frequency domain to detect FC and NFC EEG signals automatically. We classify EEG signals as FC and NFC using the proposed wavelet based system. We compute various entropies from the wavelet coefficients of the signals. These entropies are used as discriminating features for the classification of FC and NFC of EEG signals. The features are ranked using Student’s t-test ranking algorithm and then fed to Least Squares-Support Vector Machine (LS–SVM) to classify the signals. Our proposed method achieved the highest classification accuracy of 94.25%. We have obtained 91.95% sensitivity and 96.56% specificity, respectively, using this method. The classification of FC and NFC of EEG signals helps in localization of the affected brain area which needs to undergo surgery.
148 citations
Cites methods from "Automated Diagnosis of Glaucoma Usi..."
...The WT has been proven to be an excellent tool for the detection and 80 classification of various biomedical signals including EEG [17, 18]....
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TL;DR: The experimental results indicate that the CNN based system can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.
Abstract: Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.
131 citations
Cites methods from "Automated Diagnosis of Glaucoma Usi..."
...We [16–18] and from the imag extracted feat potential for others we can compressed fe B-splines coe extracted from with a feature SVM classifi [23] or with a inations for des detailed and a tonome such as scanni nd scanning la ys, only OCT e in glaucoma dus imaging aid in the de cameras in lains the int the subjective signs is a cha this difficulty rithms based anges in the o-retinal rim th shows some...
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...(2017) [23] 2D EWT and LS-SVM Private (30-/30 + ) RIM-ONE (255-/250 + ) Acc – 98....
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TL;DR: The developed methodology can be used in mass cardiac screening and can aid cardiologists in performing diagnosis as well as improve the classification accuracy up to fourth level of decomposition.
112 citations
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"Automated Diagnosis of Glaucoma Usi..." refers background in this paper
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"Automated Diagnosis of Glaucoma Usi..." refers methods in this paper
...Index Terms—Correntropy, empirical wavelet transform (EWT), feature selection, glaucoma, least-squares support vector machine (LS-SVM) classifier....
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...For classification of two classes using LS-SVM, discrimination function can be written as [27] κ(x) = sign [ ΩT z(p) + b ] (9) where Ω is weight vector of dimension x and b is a bias and z(p) function maps p into x-dimensional space....
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TL;DR: A method is presented for automated segmentation of vessels in two-dimensional color images of the retina based on extraction of image ridges, which coincide approximately with vessel centerlines, which is compared with two recently published rule-based methods.
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3,416 citations
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Book•
12 Nov 2002TL;DR: Support Vector Machines Basic Methods of Least Squares Support Vector Machines Bayesian Inference for LS-SVM Models Robustness Large Scale Problems LS- sVM for Unsupervised Learning LS- SVM for Recurrent Networks and Control.
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2,983 citations