research-article

Glaucoma Detection from Retinal Images using Generic Features: Analysis & Results

Authors:

Mennato-Allah Talaat,

Romisaa Ashraf,

Mohammad Essam,

Rana Y. ElKashlan,

Lamiaa Abdel-HamidAuthors Info & Claims

ICWIP 2019: Proceedings of the 2019 2nd International Conference on Watermarking and Image Processing

Pages 10 - 15

https://doi.org/10.1145/3369973.3369976

Published: 04 February 2020 Publication History

Abstract

Glaucoma is a silent disease that, left untreated, causes severe visual impairments which can progress to irreversible blindness. Fortunately, early detection and proper treatment can control the development of glaucoma and in turn limit further progression of associated visual impairments. However, periodic manual diagnosis of glaucoma necessary for its early diagnosis would require abundancy of experts, besides being invasive, expensive, and time consuming. Computer aided diagnosis (CAD) can thus serve as a game changer in the early detection of glaucoma by bringing clinician to the level of an expert. Moreover, CAD has the advantages of being non-invasive, simple, and cost effective. In this work, an automated generic glaucoma detection algorithm is presented in which statistical and textural features are computed from the optic nerve head (ONH) region within retinal images. Several analyses are performed to compare glaucoma classification performance considering different contrast enhancement techniques (histogram equalization - contrast limited adaptive histogram equalization) and color models (RGB - HSV - CIELAB). Feature selection is then used to find the best set of features for each of the different experiments. Best performance was achieved when textural features were computed from the histogram equalized CIELAB channels, resulting in an accuracy of 92.5%, sensitivity of 95.0%, and specificity of 90.0% considering a public dataset consisting of 40 glaucomatous and 40 healthy retinal images.

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Cited By

Pinheiro HCamilo EParanhos AFonseca ALaureano Gda Costa R(2024)Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysisArray10.1016/j.array.2024.10035923(100359)Online publication date: Sep-2024
https://doi.org/10.1016/j.array.2024.100359
M RR L(2021)Combined Diagnosis of Diabetic Retinopathy and Glaucoma Using Non-Linear Features2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP)10.1109/ICCCSP52374.2021.9465505(1-6)Online publication date: 24-May-2021
https://doi.org/10.1109/ICCCSP52374.2021.9465505
Abdel‐Hamid L(2021) TWEEC : Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques International Journal of Imaging Systems and Technology10.1002/ima.2262132:1(387-401)Online publication date: 4-Jul-2021
https://doi.org/10.1002/ima.22621

Index Terms

Glaucoma Detection from Retinal Images using Generic Features: Analysis & Results
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

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cover image ACM Other conferences

ICWIP 2019: Proceedings of the 2019 2nd International Conference on Watermarking and Image Processing

September 2019

62 pages

ISBN:9781450372800

DOI:10.1145/3369973

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 04 February 2020

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ICWIP 2019

ICWIP 2019: 2019 2nd International Conference on Watermarking and Image Processing

September 18 - 20, 2019

Marseille, France

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Cited By

Pinheiro HCamilo EParanhos AFonseca ALaureano Gda Costa R(2024)Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysisArray10.1016/j.array.2024.10035923(100359)Online publication date: Sep-2024
https://doi.org/10.1016/j.array.2024.100359
M RR L(2021)Combined Diagnosis of Diabetic Retinopathy and Glaucoma Using Non-Linear Features2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP)10.1109/ICCCSP52374.2021.9465505(1-6)Online publication date: 24-May-2021
https://doi.org/10.1109/ICCCSP52374.2021.9465505
Abdel‐Hamid L(2021) TWEEC : Computer‐aided glaucoma diagnosis from retinal images using deep learning techniques International Journal of Imaging Systems and Technology10.1002/ima.2262132:1(387-401)Online publication date: 4-Jul-2021
https://doi.org/10.1002/ima.22621

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