title: Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques.
abstract: Glaucoma is a chronic and irreversible neuro-degenerative disease in which the neuro-retinal nerve that connects the eye to the brain (optic nerve) is progressively damaged and patients suffer from vision loss and blindness. The timely detection and treatment of glaucoma is very crucial to save patient's vision. Computer aided diagnostic systems are used for automated detection of glaucoma that calculate cup to disc ratio from colored retinal images. In this article, we present a novel method for early and accurate detection of glaucoma. The proposed system consists of preprocessing, optic disc segmentation, extraction of features from optic disc region of interest and classification for detection of glaucoma. The main novelty of the proposed method lies in the formation of a feature vector which consists of spatial and spectral features along with cup to disc ratio, rim to disc ratio and modeling of a novel mediods based classier for accurate detection of glaucoma. The performance of the proposed system is tested using publicly available fundus image databases along with one locally gathered database. Experimental results using a variety of publicly available and local databases demonstrate the superiority of the proposed approach as compared to the competitors.
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi, Pakistan. usmakram@gmail.com.
authors.forename: M Usman
authors.initials: MU
authors.lastname: Akram
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi, Pakistan.
authors.forename: Anam
authors.initials: A
authors.lastname: Tariq
authors.affiliation: Computer Engineering Department, Bahria University, Islamabad, Pakistan.
authors.forename: Shehzad
authors.initials: S
authors.lastname: Khalid
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi, Pakistan.
authors.forename: M Younus
authors.initials: MY
authors.lastname: Javed
authors.affiliation: Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi, Pakistan.
authors.forename: Sarmad
authors.initials: S
authors.lastname: Abbas
authors.affiliation: Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan.
authors.forename: Ubaid Ullah
authors.initials: UU
authors.lastname: Yasin
mesh_terms: D003941:Diagnostic Techniques, Ophthalmological; D005654:Fundus Oculi; D005901:Glaucoma / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009898:Optic Disk / Q000473:pathology*
keywords: Feature extraction; Fundus images; Glaucoma; Mediods classification; Optic disc localization