Automatic Classification of Glaucoma Stages Using Two-Dimensional Tensor Empirical Wavelet Transform

Glaucoma is a chronic eye disease, causing damage to the optic nerve; it may cause permanent vision loss. The conventional instrument methods for glaucoma detection are manual and time-consuming. Many approaches have recently been proposed for automatic glaucoma classification using retinal fundus images. However, none of the existing methods can efficiently use for early-stage glaucoma detection. In this letter, we proposed a novel method for glaucoma classification based on the newly introduced two-dimensional tensor empirical wavelet transform (2D-T-EWT). In this study, the pre-processed images are decomposed into sub-band images (SBIs) using 2D-T-EWT. Then, texture-based grey level co-occurrence matrix (GLCM), chip histogram, and moment invariant features have been extracted from decomposed SBIs. Afore, robust features have been selects and ranked using the student'st-test algorithm. Finally, trained multi-class least squares-support vector machine (MC-LS-SVM) classifier has been used for the classification. The experimental results show that our method outperformed state-of-the-art approaches for glaucoma classification. The proposed method achieved the highest classification accuracy of 93.65% using tenfold cross-validation.