Glaucoma Detection Using Fundus Images of the Retina

Research Article
Open access

Glaucoma Detection Using Fundus Images of the Retina

Adithya N A. 1 , Anusha S S. 2 , Arpitha S. 3 , Lakshitha P. 4 , Divya C D. 5*
  • 1 Vidyavardhaka College of Engineering,Department of Computer Science and Engineering,Gokulam, 3rd stage, Vijayanagar, Mysuru 570002, India    
  • 2 Vidyavardhaka College of Engineering,Department of Computer Science and Engineering,Gokulam, 3rd stage, Vijayanagar, Mysuru 570002, India    
  • 3 Vidyavardhaka College of Engineering,Department of Computer Science and Engineering,Gokulam, 3rd stage, Vijayanagar, Mysuru 570002, India    
  • 4 Vidyavardhaka College of Engineering,Department of Computer Science and Engineering,Gokulam, 3rd stage, Vijayanagar, Mysuru 570002, India    
  • 5 Vidyavardhaka College of Engineering,Department of Computer Science and Engineering,Gokulam, 3rd stage, Vijayanagar, Mysuru 570002, India    
  • *corresponding author divyacd@vvce.ac.in
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220641
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

In the paper that we are proposing we make use of the image processing technique that can be further used to detect or predict whether the eye is glaucomatic which leads to increase in the size of the optic cup in turn affecting the optic disc. Glaucoma being the second major reason for blindness was very difficult to detect in the early stages. Here in this paper, we make use of the retinal fundus images and glaucoma is being detected using the features that we extract from the fundus images. The first feature that we extract includes one of the basic physiological parameters which is the Cup to Disc ratio (CDR) and the second feature that we extract is the neuro retinal rim which has the inferior, superior, nasal and the temporal quadrants often called the ISNT quadrants which can be used to detect glaucoma in the fundus image of the eye.

Keywords:

Glaucoma, Fundus Images, Cup to Disc Ratio (CDR).

A.,A.N.;S.,A.S.;S.,A.;P.,L.;D.,D.C. (2023). Glaucoma Detection Using Fundus Images of the Retina. Applied and Computational Engineering,2,785-792.
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References

[1]. Detection of Glaucoma Using Retinal Fundus Images Hafsah Ahmad, Abubakar Yamin, Aqsa Shakeel, Syed Omer Gillani, Umer Ansari School of Mechanical and Manufacturing Engi-neering National University of Sciences and Technology, Islamabad, Pakistan,2014.

[2]. Neuroretinal rim Quantification in Fundus Images to Detect Glaucoma S. Kavitha, S. Karthikeyan, Dr.K. Duraiswamy, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.6, June 2010.

[3]. Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Fea-tures Extracted from Fundus Images Shishir Maheshwari, Ram Bilas Pachori, and U. Ra-jendra Acharyan, IEEE Journal of Biomedical and Health Informatics March 2016.

[4]. Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds Ahmed Wasif Reza & C. Eswaran & Subhas Hati, March 2017.

[5]. Detection of Optic Disc and Cup from Color Retinal Images for Automated Diagnosis of Glau-coma, Megha Lotankar, Kevin Noronha, Jayasudha Koti, 2015 IEEE UP Section Confer-ence on Electrical Computer and Electronics, 2015 IEEE UP SectionConference on Electri-cal Computer and Electronics (UPCON).

[6]. Determination for Glaucoma Disease Based on Red Area Percentage Mohammad Aloudat and Miad Faezipour Departments of Computer Science & Engineering and Biomedical Engi-neering University of Bridgeport, CT 06604, USA, 2016.

[7]. Early Detection of Glaucoma Disease In Retinal Fundus Images Using Spatial FCM With Lev-el Set Segmentation B. Sudha, Surjeet Dalal, Kathiravan Srinivasan, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-5C, May 2019 India.

[8]. Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Fea-tures Extracted from Fundus Images Shishir Maheshwari, Ram Bilas Pachori, and U. Ra-jendra Acharyan, IEEE Journal of Biomedical and Health Informatics March i2016.

[9]. Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds Ahmed Wasif Reza & C. Eswaran & Subhas Hati, March 2017.

[10]. Detection of Optic Disc and Cup from Color Retinal Images for Automated Diagnosis of Glau-coma, Megha Lotankar, Kevin Noronha, Jayasudha Koti, 2015 IEEE UP Section Confer-ence on Electrical Computer and Electronics, 2015 IEEE UP Section Conference on Electri-cal Computer and Electronics (UPCON).

[11]. Determination for Glaucoma Disease Based on Red Area Percentage Mohammad Aloudat and Miad Faezipour Departments of Computer Science & Engineering and Biomedical Engi-neering University of Bridgeport, CT 06604, USA, 2016.

[12]. Early Detection Of Glaucoma Disease In Retinal Fundus Images Using Spatial FCM With Level Set Segmentation B. Sudha, Surjeet Dalal, Kathiravan Srinivasan, International Jour-nal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Is-sue-5C, May 2019 India.

[13]. Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning Detection of Glaucoma by AbbasQ, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 6, 2017.

[14]. Classification of Glaucoma Based on Texture Features Using Neural Networks Deepti Yadav, M. Partha Sarathi, Malay Kishore Dutta Department of Electronics and Communication En-gineering, Amity School of Engineering and technology, Amity University, Noida, Uttar Pradesh, INDIA, 2014.

[15]. Glaucoma Detection based on Deep Learning Network in Fundus Images, Huazhu Fu, Jun Cheng, Yanwu Xu, Jiang Liu, September I, 2019.

[16]. Detection of Glaucoma Disease from Optical Images Using Image Processing and Machine Learning Techniques, Kajal Patel, International Journal of Engineering Science Invention (IJESI), Volume 8 Issue 08 Series. II, Aug 2019, PP 35-40.

Cite this article

A.,A.N.;S.,A.S.;S.,A.;P.,L.;D.,D.C. (2023). Glaucoma Detection Using Fundus Images of the Retina. Applied and Computational Engineering,2,785-792.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Detection of Glaucoma Using Retinal Fundus Images Hafsah Ahmad, Abubakar Yamin, Aqsa Shakeel, Syed Omer Gillani, Umer Ansari School of Mechanical and Manufacturing Engi-neering National University of Sciences and Technology, Islamabad, Pakistan,2014.

[2]. Neuroretinal rim Quantification in Fundus Images to Detect Glaucoma S. Kavitha, S. Karthikeyan, Dr.K. Duraiswamy, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.6, June 2010.

[3]. Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Fea-tures Extracted from Fundus Images Shishir Maheshwari, Ram Bilas Pachori, and U. Ra-jendra Acharyan, IEEE Journal of Biomedical and Health Informatics March 2016.

[4]. Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds Ahmed Wasif Reza & C. Eswaran & Subhas Hati, March 2017.

[5]. Detection of Optic Disc and Cup from Color Retinal Images for Automated Diagnosis of Glau-coma, Megha Lotankar, Kevin Noronha, Jayasudha Koti, 2015 IEEE UP Section Confer-ence on Electrical Computer and Electronics, 2015 IEEE UP SectionConference on Electri-cal Computer and Electronics (UPCON).

[6]. Determination for Glaucoma Disease Based on Red Area Percentage Mohammad Aloudat and Miad Faezipour Departments of Computer Science & Engineering and Biomedical Engi-neering University of Bridgeport, CT 06604, USA, 2016.

[7]. Early Detection of Glaucoma Disease In Retinal Fundus Images Using Spatial FCM With Lev-el Set Segmentation B. Sudha, Surjeet Dalal, Kathiravan Srinivasan, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Issue-5C, May 2019 India.

[8]. Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Fea-tures Extracted from Fundus Images Shishir Maheshwari, Ram Bilas Pachori, and U. Ra-jendra Acharyan, IEEE Journal of Biomedical and Health Informatics March i2016.

[9]. Automatic Tracing of Optic Disc and Exudates from Color Fundus Images Using Fixed and Variable Thresholds Ahmed Wasif Reza & C. Eswaran & Subhas Hati, March 2017.

[10]. Detection of Optic Disc and Cup from Color Retinal Images for Automated Diagnosis of Glau-coma, Megha Lotankar, Kevin Noronha, Jayasudha Koti, 2015 IEEE UP Section Confer-ence on Electrical Computer and Electronics, 2015 IEEE UP Section Conference on Electri-cal Computer and Electronics (UPCON).

[11]. Determination for Glaucoma Disease Based on Red Area Percentage Mohammad Aloudat and Miad Faezipour Departments of Computer Science & Engineering and Biomedical Engi-neering University of Bridgeport, CT 06604, USA, 2016.

[12]. Early Detection Of Glaucoma Disease In Retinal Fundus Images Using Spatial FCM With Level Set Segmentation B. Sudha, Surjeet Dalal, Kathiravan Srinivasan, International Jour-nal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8 Is-sue-5C, May 2019 India.

[13]. Glaucoma-Deep: Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning Detection of Glaucoma by AbbasQ, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 6, 2017.

[14]. Classification of Glaucoma Based on Texture Features Using Neural Networks Deepti Yadav, M. Partha Sarathi, Malay Kishore Dutta Department of Electronics and Communication En-gineering, Amity School of Engineering and technology, Amity University, Noida, Uttar Pradesh, INDIA, 2014.

[15]. Glaucoma Detection based on Deep Learning Network in Fundus Images, Huazhu Fu, Jun Cheng, Yanwu Xu, Jiang Liu, September I, 2019.

[16]. Detection of Glaucoma Disease from Optical Images Using Image Processing and Machine Learning Techniques, Kajal Patel, International Journal of Engineering Science Invention (IJESI), Volume 8 Issue 08 Series. II, Aug 2019, PP 35-40.

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