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

Content Provider	IET Digital Library
Author	Khalil, Tehmina Akram, Muhammad Usman 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.
Starting Page	693
Ending Page	700
Page Count	8
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 9, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0812
Journal	IET Image Processing
Publisher Date	2017-05-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Measurement And Imaging Computer Vision And Image Processing Technique Cup-to-disc Ratio Calculation Method Decision-making Process Diseases Eye Eye Disorders Feature Extraction Fundus Image Glaucoma Condition Glaucoma Diagnosis Glaucoma Referral Health Physics HSF Hybrid Structural Hybrid Structural Feature-set Hybrid Texture Feature-set Image Segmentation Image Texture Improved Automated Detection Knowledge Engineering Technique Medical And Biomedical Uses of Field Medical Image Processing Optic Nerve Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Radiations Radioactivity Reliable Computer-aided Diagnosis System Single Eye Condition Support Vector Machine SVM
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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