NDLI: Autonomous Glaucoma detection from fundus image using cup to disc ratio and hybrid features

Content Provider	IEEE Xplore Digital Library
Author	Salam, A.A. Akram, M.U. Wazir, K. Anwar, S.M. Majid, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Eng., Univ. of Eng. & Technol., Taxila, Pakistan (Anwar, S.M.; Majid, M.) \|\| Dept. of Comput. Eng., Nat. Univ. of Sci. & Technol. Islamabad, Islamabad, Pakistan (Salam, A.A.; Akram, M.U.)
Abstract	Glaucoma is a non-curable optic disease which can cause irreversible blindness if not detected at early stage. Progression of glaucoma occurs due to an increase in intraocular pressure and results in the damage of optic nerve. Progression of glaucoma can be stopped if detected at an early stage. There are no early symptoms of glaucoma and the only source to detect glaucoma at an early stage is the structural change that arises in the internal eye. Fundoscopy is one of the modern medical imaging techniques that enable Ophthalmologists to observe structural changes in the Optic Disc to detect glaucoma. Many autonomous glaucoma detection systems analyze fundus image by calculating Cup to Disc Ratio (CDR) and categorize the image as glaucoma or healthy. Glaucoma detection using machine learning is also being used widely to aid ophthalmologists. The proposed methodology provides a novel algorithm to detect glaucoma using a fusion of CDR and hybrid textural and intensity features. Image categorization (glaucoma, non-glaucoma, suspect) is done based on the results from both CDR and classifier. This fusion of CDR with hybrid features has improved the sensitivity of system to 1, specificity 0.88 and accuracy 92%.
Starting Page	370
Ending Page	374
File Size	360013
Page Count	5
File Format	PDF
e-ISBN	9781509004812
DOI	10.1109/ISSPIT.2015.7394362
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-07
Publisher Place	United Arab Emirates
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optical imaging Feature extraction Biomedical optical imaging Image color analysis Optical sensors Image segmentation Histograms hybridfeatures fundus Machine Learning Cup to disc ratio
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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