Segmentation technique and dynamic ensemble selection to enhance glaucoma severity detection

Abstract

The severity of glaucoma can be observed by categorising glaucoma diseases into several classes based on a classification process. The two most suitable parameters, cup-to-disc ratio (CDR) and peripapillary atrophy (PPA), which are commonly used to identify glaucoma are utilized in this study to strengthen the classification. First, an active contour snake (ACS) is employed to retrieve both optic disc (OD) and optic cup (OC) values, which are required to calculate the CDR. Moreover, Otsu segmentation and thresholding techniques are used to identify PPA, and the features are then extracted using a grey-level co-occurrence matrix (GLCM). An advanced segmentation technique, combined with an improved classifier called dynamic ensemble selection (DES), is proposed to classify glaucoma. Because DES is generally used to handle an imbalanced dataset, the proposed model is expected to detect glaucoma severity and determine the subsequent treatment accurately. The proposed model obtains a higher mean accuracy (0.96) than the deep learning-based U-Net (0.90) when evaluated using three datasets of 250 retinal fundus images (200 training, 50 testings) based on the 5-fold cross-validation scheme.

Keywords: Active contour snake; Dynamic ensemble selection; Glaucoma severity; Multiclass classification; Segmentation technique.