Improved automated detection of glaucoma from fundus image using hybrid structural and textural features. Issue 9 (8th August 2017)

• Record Type: Journal Article
• Authors: Khalil, Tehmina; Usman Akram, Muhammad; Khalid, Samina; Jameel, Amina
• Is Part Of: IET image processing. Volume 11:Issue 9(2017)
• Page Start: 693
• Page End: 700
• Publication Date: 2017-08-08
• Subjects: feature extraction -- image texture -- eye -- diseases -- support vector machines -- image segmentation -- medical image processing; improved automated detection -- fundus image -- hybrid structural -- eye disorders -- optic nerve -- single eye condition -- glaucoma diagnosis -- reliable computer‐aided diagnosis system -- decision‐making process -- glaucoma conditions -- hybrid structural feature‐set -- HSF -- hybrid texture feature‐set -- support vector machine -- SVM -- cup‐to‐disc ratio calculation method -- glaucoma referral; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0812
• Languages: English
• ISSNs: 1751-9659

Abstract

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.