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

Abstract

Eye is a precious part of human body. But in the world, glaucoma has affected the human eye as second main cause of blindness. This disease grows very slowly in the eye and without noticing it destroyed the optic verves within the eye. Traditional glaucoma detection methods are costly and time consuming. Therefore, better methods are required to detect glaucoma more accurately. Hence, a new adaptive approach for glaucoma detection using bidimensional empirical mode decomposition (BDEMD) from retinal images is proposed. Adaptive bi-dimensional intrinsic mode functions (BDIMFs) are obtained using very simple steps, from pre-processed and coloured decomposed images, is the main idea of the proposed method. Moment and texture features contain more information hence these are extracted from decomposed BDIMFs. These features are then normalized and classified by support vector machine (SVM) with its different kernel functions. The achieved results like, accuracy, sensitivity, and specificity are 97.02 %, 98.23 %, 95.46 %, respectively for 10-fold cross validation. Experimental analysis shows that our method outperformed over the traditional methods for glaucoma detection.