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

Abstract

Glaucoma is one of the foremost causes of blindness over the world. It develops slowly and damages optic nerve head. Existing methods of glaucoma detection are expensive and sluggish. Hence quick and low-priced methods are required. In this paper, a novel fully variational and adaptive computer based glaucoma detection using compact variational mode decomposition (CVMD) from fundus images is proposed. Efficient sub band images having narrow fourier bandwidth, clear and sharp boundaries are obtained using CVMD is the key idea of the proposed method. This gives robust estimate of features. Texture feature capture subtle variation and give more information therefore these features are extracted from efficient sub band images and helps to increase the glaucoma detection accuracy. Extracted features are normalized and classified using support vector machine classifier. The obtained classification accuracies are 86.67 %, 86.67 %, 85.42% and 89.18 % for three, five, eight and tenfold cross validation respectively with kernel parameter 2. The obtained accuracy, sensitivity, specificity, precision and Fmeasure are 89.18 %, 90 %, 85 %, 93.34 % and 89.34 % respectively for tenfold cross validation. Proposed method is found to be better compared to the existing.

Exudate identification in retinal fundus images using precise textural verifications

“…Since the green channel of retinal fundus image has the highest contrast, firstly the green channel of image is considered for processing operations. This point is similar to what has been considered in 32 – 36 . Also, normalization process is applied on the image.…”

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

“…There are several classification methods available for this process. We refer to the previous researches [12,[20][21][22][23][24][25] for the selection of the classification method using 2 parameters, which are and gamma (). Both parameters are suitable for the support vector machine (SVM) method.…”

Glaucoma Detection using Bi-Dimensional Empirical Mode Decomposition from Retinal Fundus Images

“…Kirar [13] proposed DWT and [14] DWT and EWT and reported an accuracy of 88.3 % and 83.57% using SVM, respectively. Kirar [15] proposed CVMD in new work and reported an accuracy of 89.18.3% using SVM. Agrawal [16] designed an automated glaucoma detection method using QB-VMD.…”

“…Kirar [13] proposed automated glaucoma detection approach using discrete wavelet transform (DWT) and [14] using DWT with EWT. Kirar [15] designed an automated glaucoma detection method using compact variational mode decomposition (CVMD). Robust features were found out using post-processing, and then classified by SVM.…”

“…Methods [10,14] are based on EWT, it overcomes the drawback of DWT based methods but due to having improper segmentation it leads to redundancy and interference. Methods [11,[15][16]22] are based on VMD, it overcomes the limitations of DWT and EWT based methods, but it also suffers from boundary effects problems to perform on larger and varied data set.…”