Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images

Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic...

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Main Author:	Rahim, Sarni Suhaila
Format:	Thesis
Language:	English
Published:	2016
Subjects:	R Medicine (General) RE Ophthalmology
Online Access:	http://eprints.utem.edu.my/id/eprint/18829/1/Automatic%20Screening%20And%20Classification%20Of%20Diabetic%20Retinopathy%20Eye%20Fundus%20Images%2024%20Pages.pdf
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id	my-utem-ep.18829
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spelling	my-utem-ep.188292017-07-31T00:56:56Z Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images 2016 Rahim, Sarni Suhaila R Medicine (General) RE Ophthalmology Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage. UTeM 2016 Thesis http://eprints.utem.edu.my/id/eprint/18829/ http://eprints.utem.edu.my/id/eprint/18829/1/Automatic%20Screening%20And%20Classification%20Of%20Diabetic%20Retinopathy%20Eye%20Fundus%20Images%2024%20Pages.pdf text en public http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000102316 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English
topic	R Medicine (General) RE Ophthalmology
spellingShingle	R Medicine (General) RE Ophthalmology Rahim, Sarni Suhaila Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images
description	Diabetic Retinopathy (DR) is a disorder of the retinal vasculature. It develops to some degree in nearly all patients with long-standing diabetes mellitus and can result in blindness. Screening of DR is essential for both early detection and early treatment. This thesis aims to investigate automatic methods for diabetic retinopathy detection and subsequently develop an effective system for the detection and screening of diabetic retinopathy. The presented diabetic retinopathy research involves three development stages. Firstly, the thesis presents the development of a preliminary classification and screening system for diabetic retinopathy using eye fundus images. The research will then focus on the detection of the earliest signs of diabetic retinopathy, which are the microaneurysms. The detection of microaneurysms at an early stage is vital and is the first step in preventing diabetic retinopathy. Finally, the thesis will present decision support systems for the detection of diabetic retinopathy and maculopathy in eye fundus images. The detection of maculopathy, which are yellow lesions near the macula, is essential as it will eventually cause the loss of vision if the affected macula is not treated in time. An accurate retinal screening, therefore, is required to assist the retinal screeners to classify the retinal images effectively. Highly efficient and accurate image processing techniques must thus be used in order to produce an effective screening of diabetic retinopathy. In addition to the proposed diabetic retinopathy detection systems, this thesis will present a new dataset, and will highlight the dataset collection, the expert diagnosis process and the advantages of the new dataset, compared to other public eye fundus images datasets available. The new dataset will be useful to researchers and practitioners working in the retinal imaging area and would widely encourage comparative studies in the field of diabetic retinopathy research. It is envisaged that the proposed decision support system for clinical screening would greatly contribute to and assist the management and the detection of diabetic retinopathy. It is also hoped that the developed automatic detection techniques will assist clinicians to diagnose diabetic retinopathy at an early stage.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rahim, Sarni Suhaila
author_facet	Rahim, Sarni Suhaila
author_sort	Rahim, Sarni Suhaila
title	Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images
title_short	Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images
title_full	Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images
title_fullStr	Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images
title_full_unstemmed	Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images
title_sort	automatic screening and classification of diabetic retinopathy eye fundus images
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18829/1/Automatic%20Screening%20And%20Classification%20Of%20Diabetic%20Retinopathy%20Eye%20Fundus%20Images%2024%20Pages.pdf
_version_	1747833950672257024

Automatic Screening And Classification Of Diabetic Retinopathy Eye Fundus Images

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