NDLI: Glaucoma screening pipeline based on clinical measurements and hidden features

Content Provider	IET Digital Library
Author	Zhao, Xin Guo, Fan Mai, Yuxiang Tang, Jin Duan, Xuanchu Zou, Beiji Jiang, Lingzi
Abstract	Glaucoma refers to a chronic disease of the eye that leads to vision loss that is irreversible, which is called ‘silent theft of sight’. Thus, an automatic glaucoma screening pipeline from optic disc (OD) localisation to glaucoma risk prediction is proposed in this study. The proposed pipeline consists of three main phases. Firstly, the OD is localised by morphological processing and sliding window methods. Secondly, a novel neural network which is in U-shape and convolutional introduces concatenating path and fusion loss function is developed to split OD and optic cup (OC) at the same time. Thirdly, both clinical measurements including optic cup-to-disc ratio (CDR), neuroretinal rim related features, and hidden features including statistical moments, entropy and energy are combined to train glaucoma classifiers. According to the results of the experiment, the proposed segmentation network achieves the best performance on both OD and OC segmentation and the proposed CDR calculation method is capable of achieving the performance similar to that of ophthalmologist on CDR measurement. Besides, the authors’ glaucoma classification model can obtain the best performance on sensitivity and area under the curve score in comparison with the existing methods.
Starting Page	2213
Ending Page	2223
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 12, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0137
Journal	IET Image Processing
Publisher Date	2019-08-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Glaucoma Screening Pipeline Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging CDR Calculation Method CDR Measurement Chronic Disease Clinical Measurement Computer Vision And Image Processing Technique Convolutional Introduces Diseases Eye Feature Extraction Fusion Loss Function Glaucoma Classifier Glaucoma Risk Prediction Hidden Feature Image Classification Image Recognition Image Segmentation Medical Image Processing Morphological Processing Neural Computing Technique Neural Nets Novel Neural Network OC Segmentation Optic Cup-to-disc Ratio Optic Disc Localisation Optical And Laser Radiation Patient Diagnostic Method And Instrumentation Physiological Optics Physiological Vision Probability Theory Segmentation Network Sliding Window Method Statistical Analysis Statistics Stochastic Linearised SCUC Vision Loss
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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