Improved automated detection of glaucoma from fundus image using hybrid structural and textural features. Issue 9 (8th August 2017) - Khalil, Tehmina; Usman Akram, Muhammad; Khalid, Samina; Jameel, Amina
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.