Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

Sami Suhaila Rahim; Vasile Palade; Chrisina Jayne; Andreas Holzinger; James Shuttleworth

doi:10.1007/978-3-319-23344-4_37

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

Sami Suhaila Rahim, Vasile Palade, Chrisina Jayne, Andreas Holzinger, James Shuttleworth

Institute of Interactive Systems and Data Science (7060)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Diabetic retinopathy is a damage of the retina and it is one of the serious consequences of the diabetes. Early detection of diabetic retinopathy is extremely important in order to prevent premature visual loss and blindness. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. The detection of maculopathy is essential as it will eventually cause loss of vision if the affected macula is not timely treated. The developed system consists of image acquisition, image preprocessing with a combination of fuzzy techniques, feature extraction, and image classification by using several machine learning techniques. The fuzzy-based image processing decision support system will assist in the diabetic retinopathy screening and reduce the burden borne by the screening team.

Original language	English
Title of host publication	Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI
Editors	Yike Guo, Karl Friston, Aldo A. Faisal, Sean Hill, Hanchuan Peng
Place of Publication	Cham, Heidelberg, New York, Dordrecht, London
Publisher	Springer International
Pages	379-388
Volume	9250
Edition	1
ISBN (Electronic)	978-3-319-23344-4
ISBN (Print)	978-3-319-23343-7
DOIs	https://doi.org/10.1007/978-3-319-23344-4_37
Publication status	Published - 2015
Event	Interactive Machine Learning with the “human-in-the-loop”: 8th international special session of the HCI-KDD expert group - Imperial College London, London, United Kingdom Duration: 1 Sept 2015 → 1 Sept 2015 http://hci-kdd.org/bih2015-interactive-machine-learning/

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	9250

Conference

Conference	Interactive Machine Learning with the “human-in-the-loop”
Abbreviated title	iML@BIH
Country/Territory	United Kingdom
City	London
Period	1/09/15 → 1/09/15
Internet address	http://hci-kdd.org/bih2015-interactive-machine-learning/

Keywords

Machine Learning
Health Informatics
Classifiers
Diabetic retinopathy
eye screening

ASJC Scopus subject areas

Artificial Intelligence

Fields of Expertise

Information, Communication & Computing

Treatment code (Nähere Zuordnung)

Basic - Fundamental (Grundlagenforschung)
Experimental

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-23344-4_37

DETECTION-RETINOPATHY-FUZZY-MACHINE-LEARNING-2015.pdfFinal published version, 1.11 MB

Cite this

Rahim, S. S., Palade, V., Jayne, C., Holzinger, A., & Shuttleworth, J. (2015). Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing. In Y. Guo, K. Friston, A. A. Faisal, S. Hill, & H. Peng (Eds.), Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI (1 ed., Vol. 9250, pp. 379-388). (Lecture Notes in Artificial Intelligence; Vol. 9250). Springer International. https://doi.org/10.1007/978-3-319-23344-4_37

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing. / Rahim, Sami Suhaila; Palade, Vasile; Jayne, Chrisina et al.
Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI. ed. / Yike Guo; Karl Friston; Aldo A. Faisal; Sean Hill; Hanchuan Peng. Vol. 9250 1. ed. Cham, Heidelberg, New York, Dordrecht, London: Springer International, 2015. p. 379-388 (Lecture Notes in Artificial Intelligence; Vol. 9250).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Rahim, SS, Palade, V, Jayne, C, Holzinger, A & Shuttleworth, J 2015, Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing. in Y Guo, K Friston, AA Faisal, S Hill & H Peng (eds), Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI. 1 edn, vol. 9250, Lecture Notes in Artificial Intelligence, vol. 9250, Springer International, Cham, Heidelberg, New York, Dordrecht, London, pp. 379-388, Interactive Machine Learning with the “human-in-the-loop”, London, United Kingdom, 1/09/15. https://doi.org/10.1007/978-3-319-23344-4_37

Rahim SS, Palade V, Jayne C, Holzinger A, Shuttleworth J. Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing. In Guo Y, Friston K, Faisal AA, Hill S, Peng H, editors, Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI. 1 ed. Vol. 9250. Cham, Heidelberg, New York, Dordrecht, London: Springer International. 2015. p. 379-388. (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-319-23344-4_37

Rahim, Sami Suhaila ; Palade, Vasile ; Jayne, Chrisina et al. / Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing. Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI. editor / Yike Guo ; Karl Friston ; Aldo A. Faisal ; Sean Hill ; Hanchuan Peng. Vol. 9250 1. ed. Cham, Heidelberg, New York, Dordrecht, London : Springer International, 2015. pp. 379-388 (Lecture Notes in Artificial Intelligence).

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abstract = "Diabetic retinopathy is a damage of the retina and it is one of the serious consequences of the diabetes. Early detection of diabetic retinopathy is extremely important in order to prevent premature visual loss and blindness. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. The detection of maculopathy is essential as it will eventually cause loss of vision if the affected macula is not timely treated. The developed system consists of image acquisition, image preprocessing with a combination of fuzzy techniques, feature extraction, and image classification by using several machine learning techniques. The fuzzy-based image processing decision support system will assist in the diabetic retinopathy screening and reduce the burden borne by the screening team.",

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AB - Diabetic retinopathy is a damage of the retina and it is one of the serious consequences of the diabetes. Early detection of diabetic retinopathy is extremely important in order to prevent premature visual loss and blindness. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. The detection of maculopathy is essential as it will eventually cause loss of vision if the affected macula is not timely treated. The developed system consists of image acquisition, image preprocessing with a combination of fuzzy techniques, feature extraction, and image classification by using several machine learning techniques. The fuzzy-based image processing decision support system will assist in the diabetic retinopathy screening and reduce the burden borne by the screening team.

KW - Machine Learning

KW - Health Informatics

KW - Classifiers

KW - Diabetic retinopathy

KW - eye screening

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BT - Brain Informatics and Health, Lecture Notes in Artificial Intelligence LNAI

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CY - Cham, Heidelberg, New York, Dordrecht, London

T2 - Interactive Machine Learning with the “human-in-the-loop”

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Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

Other files and links

Fingerprint

Interactive Machine Learning with the “human-in-the-loop”

Cite this

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Fields of Expertise

Treatment code (Nähere Zuordnung)

UN SDGs

Access to Document

Other files and links

Fingerprint

Activities

Interactive Machine Learning with the “human-in-the-loop”

Cite this