Improved automated detection of glaucoma from fundus image using hybrid structural and textural features

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dc.contributor.author Tehmina Khalil
dc.contributor.author Muhammad Usman Akram
dc.contributor.author Samina Khalid
dc.contributor.author Amina Jameel
dc.date.accessioned 2018-08-08T06:35:12Z
dc.date.available 2018-08-08T06:35:12Z
dc.date.issued 2017
dc.identifier.issn 1751-9659
dc.identifier.uri http://hdl.handle.net/123456789/7172
dc.description.abstract Glaucoma is a group of eye disorders that damage the optic nerve. Considering a single eye condition for the diagnosis of glaucoma has failed to detect all glaucoma cases accurately. A reliable computer-aided diagnosis system is proposed based on a novel combination of hybrid structural and textural features. The system improves the decision-making process after analysing a variety of glaucoma conditions. It consists of two main modules hybrid structural feature-set (HSF) and hybrid texture feature-set (HTF). HSF module can classify a sample using support vector machine (SVM) from different structural glaucoma condition and the HTF module analyses the sample founded on various texture and intensity-based features and again using SVM makes a decision. In the case of any conflict in the results of both modules, a suspected class is introduced. A novel algorithm to compute the super-pixels has also been proposed to detect the damaged cup. This feature alone outperformed the current state-of-the-art methods with 94% sensitivity. Cup-to-disc ratio calculation method for cup and disc segmentation, involving two different channels has been introduced increasing the overall accuracy. The proposed system has given exceptional results with 100% accuracy for glaucoma referral. en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.relation.ispartofseries ;10.1049/iet-ipr.2016.0812
dc.subject Computer Engineering en_US
dc.title Improved automated detection of glaucoma from fundus image using hybrid structural and textural features en_US
dc.type Article en_US


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