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.
journal_name
Australas Phys Eng Sci Medjournal_title
Australasian physical & engineering sciences in medicineauthors
Akram MU,Tariq A,Khalid S,Javed MY,Abbas S,Yasin UUdoi
10.1007/s13246-015-0377-ysubject
Has Abstractpub_date
2015-12-01 00:00:00pages
643-55issue
4eissn
0158-9938issn
1879-5447pii
10.1007/s13246-015-0377-yjournal_volume
38pub_type
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journal_title：Australasian physical & engineering sciences in medicine
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