Optimal Hyper Analytic Wavelet Transform for Glaucoma Detection in Fundal Retinal Images

Abstract

-Glaucoma is one of the most common causes of blindness which is caused by increase of fluid pressure in the eye which damages the optic nerve and eventually causing vision loss. An automated technique to diagnose glaucoma disease can reduce the physicians' effort in screening of Glaucoma in a person through the fundal retinal images. In this paper, optimal hyper analytic wavelet transform for Glaucoma detection technique from fundal retinal images is proposed. The optimal coefficients for transformation process are found out using the hybrid GSO-Cuckoo search algorithm. This technique consists of pre-processing module, optimal transformation module, feature extraction module and classification module. The implementation is carried out with MATLAB and the evaluation metrics employed are accuracy, sensitivity and specificity. Comparative analysis is carried out by comparing the hybrid GSO with the conventional GSO. The results reported in our paper show that the proposed technique has performed well and has achieved good evaluation metric values. Two 10-fold cross validated test runs are performed, yielding an average fitness of 91.13% and 96.2% accuracy with CGD-BPN (Conjugate Gradient Descent-Back Propagation Network) and Support Vector Machines (SVM) respectively. The techniques also gives high sensitivity and specificity values. The attained high evaluation metric values show the efficiency of detecting Glaucoma by the proposed technique.

Current Research on Glaucoma Detection using Compact Variational Mode Decomposition from Fundus Images

“…The obtained accuracy, specificity and sensitivity are 85 %, 100 % and 82 % respectively. Furthermore Raja [9] proposed another technique for automated detection of glaucoma using OHAWT and SVM. The method was performed in 4 steps namely, pre-processing, optimal transformation, feature extraction and classification.…”

“…Entropy and energy features are extracted and ANN yielded an accuracy of 85%. Raja [9] proposed a technique for automated detection of glaucoma using optimal hyper analytic wavelet transform (OHAWT) and support vector International Journal of Intelligent Engineering and Systems, Vol. 12, No.3, 2019 DOI: 10.22266/ijies2019.0630.01 machine (SVM).…”

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

“…The reported accuracy, sensitivity and specificity are 85, 82 and 100%, respectively, from 158 images of Medical Image Analysis Group (MIAG) image database. Raja and Gangatharan [20] used the optimal hyper analytic wavelet transform (OHAWT) and SVM. The reported accuracy is 85% from 169 images of MIAG image database.…”

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

“…Reduced features were fed to SVM which yielded an accuracy of 80% from 575 EGR images. Raja and Gangatharan [22–24] proposed glaucoma detection methods. The reported accuracies are 81, 85, and 85% using CWT, WPD, and OHAWT, respectively.…”

“…They obtained an accuracy of 85% from 158 images. Raja and Gangatharan [24] decomposed images using optimal hyper‐analytic wavelet transform (OHAWT) and fed to SVM. They reported an accuracy of 85%.…”