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This work aims to develop a hybrid image examination system to extract and evaluate the optic disc (OD) from the age-related macular degeneration (AMD) and non-AMD class digital fundus retinal image (DFRI). This work implements an image pre-processing through Shannon’s entropy and social group optimization (SE+SGO)-based thresholding and image post-processing with level set segmentation (LSS). A relative study among the extracted OD and the ground-truth is then executed to compute the vital picture similarity parameters (PSP). This study also presents a detailed pixel level data analysis practice on the extracted OD. Finally, the performance of the LSS is then validated against the existing segmentation techniques, such as Chan-Vese, active-contour, and k-means clustering. The work is executed on the iChallenge-AMD-2018 DFRI (400 images), and the results confirm that the proposed hybrid tool helps to achieve better values of Jaccard (86.82%), dice (91.78%), accuracy (98.94%), precision (92.86%), sensitivity (94.06%), and specificity (99.46%).

An Image Examination System for Retinal Optic Disc Mining and Analysis With Social Group Optimization Algorithm