Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images

Kirar, Bhupendra Singh; Agrawal, Dheeraj

doi:10.1049/iet-ipr.2018.5297

Cited by 59 publications

(28 citation statements)

References 36 publications

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“…They reported an accuracy, sensitivity, and specificity of 81.22, 93.62, and 95.88%, respectively using ten FCV from 505 MIAG fundus images [43]. Kirar and Agrawal [26] proposed a CAD‐based automated glaucoma diagnosis using DWT and EWT. They reported an accuracy, sensitivity, and specificity of 83.57, 86.4, and 80.8%, respectively using ten FCV from 505 MIAG fundus images [43].…”

Section: Resultsmentioning

confidence: 99%

“…This paper used three performance measures; accuracy (Ac), sensitivity (Se), and specificity (Sp) using (12)–(14) [26, 42].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Seventy features are extracted from QB‐VMD SBIs. The extracted features are normalised using zero one normalisation within the interval [0, 1] using (7) [26].

p_{i m}^{n} = \frac{(][, p_{i m} - min ()(, p_{i m}))}{(][, max ()(, p) - min ()(, p_{i m}))}

where

p_{i m}

is the actual value of features, and

p_{i m}^{n}

is the normalised value of features, m is the feature, i is the sample,

max

is the maximum values of features, and

min

is the minimum values of features.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…They reported accuracy of 81.22% for ten FCV from 505 MIAG fundus images. Kirar and Agrawal [26] proposed a CAD of glaucoma using discrete wavelet transform (DWT) and EWT. They used hybrid and concatenation approach in which 77 features were extracted from DWT, EWT, and their combinations.…”

Section: Introductionmentioning

confidence: 99%

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Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images

2019

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Glaucoma is a critical and irreversible neurodegenerative eye disorder caused by damaging optical nerve head due to increased intra‐ocular pressure within the eye. Detection of glaucoma is a critical job for ophthalmologists. This study presents a novel and more accurate method for automated glaucoma detection using quasi‐bivariate variational mode decomposition (QB‐VMD) from digital fundus images. In total, 505 fundus images are decomposed using QB‐VMD method which gives band limited sub‐band images (SBIs) centred around a particular frequency. These SBIs are smooth and free from mode mixing problems. The glaucoma detection accuracy depends on the most useful features as it captured appropriate information. Seventy features are extracted from QB‐VMD SBIs. Extracted features are normalised and selected using ReliefF method. Selected features are then fed to singular value decomposition to reduce their dimensionality. Finally, the reduced features are classified using least square support vector machine classifier. The obtained glaucoma detection accuracies are 85.94 and 86.13% using three‐ and ten‐fold cross validation, respectively. Obtained results are better than the existing. It may become a suitable method for ophthalmologists to examine eye disease more accurately using fundus images.

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Section: Resultsmentioning

confidence: 99%

“…This paper used three performance measures; accuracy (Ac), sensitivity (Se), and specificity (Sp) using (12)–(14) [26, 42].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Seventy features are extracted from QB‐VMD SBIs. The extracted features are normalised using zero one normalisation within the interval [0, 1] using (7) [26].

p_{i m}^{n} = \frac{(][, p_{i m} - min ()(, p_{i m}))}{(][, max ()(, p) - min ()(, p_{i m}))}

where

p_{i m}

is the actual value of features, and

p_{i m}^{n}

is the normalised value of features, m is the feature, i is the sample,

max

is the maximum values of features, and

min

is the minimum values of features.…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images

2019

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“…We employed the wavedec2 function in MATLAB to implement the DWT, and the biorthogonal wavelet bior1.5 was used. Of note, DWT has also been employed for the classification of fundus images of glaucoma [24], [38]. Since images are of size 512 × 128 pixels, we performed the 7-level (log 2 (128) = 7) DWT.…”

Section: Methodsmentioning

confidence: 99%

Wavelet Features of the Thickness Map of Retinal Ganglion Cell-Inner Plexiform Layer Best Discriminate Prior Optic Neuritis in Patients With Multiple Sclerosis

Zhang

Wang

et al. 2020

IEEE Access

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The goal of this study was to analyze wavelet features of topographic thickness maps of the retinal ganglion cell-inner plexiform layer (GCIPL) and evaluate their discrimination ability in patients with multiple sclerosis (MS) and a history of optic neuritis (ON). Twenty-nine patients with relapsing-remitting MS and a history of ON were recruited together with 63 age-and sex-matched controls (HC). There were 33 eyes with a history of ON (MSON), 25 non-ON fellow eyes (MSFE) and 63 HC eyes. Ultrahighresolution optical coherence tomography was used to image the macula of each participant, and the volumetric dataset was segmented to yield a topographic thickness map of the GCIPL. The thickness map was analyzed using discrete wavelet transform to extract useful features, which were further analyzed with three machine learning methods, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), and support vector machine (SVM), for classification between groups. LR-EN with nested leave-one-out cross-validation resulted in 94% (83%) sensitivity and 100% (97%) specificity in discriminating MSON (MSFE) eyes from HC eyes in the training data and 88% (64%) sensitivity and 94% (94%) specificity in discriminating MSON (MSFE) eyes from HC eyes in the validation data, which were similar to or better than those resulting from LR and SVM. LR-EN improved the sensitivity and specificity of discriminating prior ON and MSFE eyes in patients with MS from HC eyes compared with traditional thickness analysis. It may be promising in facilitating the diagnosis of subclinical ON in patients with MS.

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Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

Ajesh¹,

Ravi²

2020

Int J Imaging Syst Tech

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Glaucoma is considered as the main source of irrevocable loss of vision. The earlier diagnosis of glaucoma is essential to provide earlier treatment and to reduce vision loss. The fundus images are transfigured in the ophthalmology and are used to visualize the structures of the optic disc. However, accuracy is considered as a major constraint. To increase accuracy, an effective optimization‐driven classifier is developed for glaucoma detection. The proposed Jaya‐chicken swarm optimization (Jaya‐CSO) is employed for training the recurrent neural network (RNN) for glaucoma detection. The proposed Jaya‐CSO is designed by integrating the Jaya algorithm with the chicken swarm optimization (CSO) technique for tuning the weights of the RNN classifier. The method utilized optic disc features, statistical features, and blood vessel features for the determination of the glaucomatous region. The features obtained from the optic disc, blood vessels, and the fundus image is formulated as a feature vector. Finally, the glaucoma classification is done using RNN using the feature vector such that the RNN is trained using the proposed Jaya‐CSO. The proposed Jaya‐CSO outperformed other existing models with maximal accuracy of 0.97, the specificity of 0.97, and sensitivity of 0.97, respectively.

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Computer aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images

Cited by 59 publications

References 36 publications

Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images

Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images

Wavelet Features of the Thickness Map of Retinal Ganglion Cell-Inner Plexiform Layer Best Discriminate Prior Optic Neuritis in Patients With Multiple Sclerosis

Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

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