Glaucoma Detection Based on Specularity Removal Low Rank Model from Retinal Fundus Images
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Abstract
Glaucoma is one of the eye diseases that affect the optic nerves which connect the eye to the human brain. Detection of glaucoma; in the early stages prevents loss of vision. Automatic detection of glaucoma; becomes a technical challenge for image processing using convex approximation. Fundus images of the eye are taken by a fundus camera through which automatic detection is possible. The retinal fundus imaging process suers from nonuniform illumination problems due to the curved surface of the retina and pupil dilation, which affects glaucoma detection. The prime objective of this research is to provide the best low-rank model for specularity removal from the retinal fundus images using the Robust PCA algorithm for better screening without losing important information. The Cup to Disc Ratio (CDR) in the fundus image is calculated from Optic Disc (OD) and Optic Cup (OC) segmentation and Support Vector Machine (SVM), a machine learning algorithm mostly popular for binary classification. We use ORIGA, Drishti-GS-RETINA, and REFUGE databases of fundus images for the experimental analysis and MATLAB implementation. The paper presents a comparison of ve RPCA algorithms, and the success rate of glaucoma detection increases to 97% using the IALM method. The proposed method provides a pre-processing step for specularity removal from fundus images and improves the glaucoma detection rate.
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