An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system

The strabismus (squint) is one of children's most common vision disorders. It can cause discomfort and have a significant detrimental effect on daily life. A timely diagnosis is needed to prevent it from getting worse. However, the traditional diagnosis screening is usually done manually and re...

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Main Author:	Zolkifli, Nur Syazlin
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.uthm.edu.my/10966/1/24p%20NUR%20SYAZLIN%20%20ZOLKIFLI.pdf http://eprints.uthm.edu.my/10966/2/NUR%20SYAZLIN%20%20ZOLKIFLI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10966/3/NUR%20SYAZLIN%20%20ZOLKIFLI%20WATERMARK.pdf
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id	my-uthm-ep.10966
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spelling	my-uthm-ep.109662024-05-15T07:19:17Z An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system 2022-11 Zolkifli, Nur Syazlin TJ Mechanical engineering and machinery The strabismus (squint) is one of children's most common vision disorders. It can cause discomfort and have a significant detrimental effect on daily life. A timely diagnosis is needed to prevent it from getting worse. However, the traditional diagnosis screening is usually done manually and requires expertise, time and high cost due to the equipment. Thus, the proposed semi-automated strabismus detection using computer-aided diagnosis can help to reduce the time for the ophthalmologist to diagnose the strabismus and the misalignment measurement. This research aims to propose the image processing approach for detection and diagnosis of strabismus. This research proposes three phases: pre-processing, feature extraction, and classification. Initially, the image in pre-processing undergoes Viola Jones algorithm, red channel extraction, contrast adjustment and median filtering to reduce the noise and enhance the image. In feature extraction, binarization and morphological operations are implemented to identify the location of the iris and the misalignment measurement. Finally, the classification is divided into two, where the coordinates of the iris and misalignment measurement are carried out using regional descriptor and line ROI, while the strabismus and non-strabismus are classified using Convolutional Neural Network (CNN). The experimental results have proven that the proposed method has successfully detected the strabismus and the misalignment measurement with an average accuracy for the Eye Disease dataset (0.9167), Google Images (0.9217), CAVE (0.9167), and SiblingsDB (0.9167). In conclusion, by utilizing the image processing approach, this system will be able to assist the ophthalmologist and health care practitioners as strabismus pre-screening tools 2022-11 Thesis http://eprints.uthm.edu.my/10966/ http://eprints.uthm.edu.my/10966/1/24p%20NUR%20SYAZLIN%20%20ZOLKIFLI.pdf text en public http://eprints.uthm.edu.my/10966/2/NUR%20SYAZLIN%20%20ZOLKIFLI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/10966/3/NUR%20SYAZLIN%20%20ZOLKIFLI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Zolkifli, Nur Syazlin An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
description	The strabismus (squint) is one of children's most common vision disorders. It can cause discomfort and have a significant detrimental effect on daily life. A timely diagnosis is needed to prevent it from getting worse. However, the traditional diagnosis screening is usually done manually and requires expertise, time and high cost due to the equipment. Thus, the proposed semi-automated strabismus detection using computer-aided diagnosis can help to reduce the time for the ophthalmologist to diagnose the strabismus and the misalignment measurement. This research aims to propose the image processing approach for detection and diagnosis of strabismus. This research proposes three phases: pre-processing, feature extraction, and classification. Initially, the image in pre-processing undergoes Viola Jones algorithm, red channel extraction, contrast adjustment and median filtering to reduce the noise and enhance the image. In feature extraction, binarization and morphological operations are implemented to identify the location of the iris and the misalignment measurement. Finally, the classification is divided into two, where the coordinates of the iris and misalignment measurement are carried out using regional descriptor and line ROI, while the strabismus and non-strabismus are classified using Convolutional Neural Network (CNN). The experimental results have proven that the proposed method has successfully detected the strabismus and the misalignment measurement with an average accuracy for the Eye Disease dataset (0.9167), Google Images (0.9217), CAVE (0.9167), and SiblingsDB (0.9167). In conclusion, by utilizing the image processing approach, this system will be able to assist the ophthalmologist and health care practitioners as strabismus pre-screening tools
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Zolkifli, Nur Syazlin
author_facet	Zolkifli, Nur Syazlin
author_sort	Zolkifli, Nur Syazlin
title	An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
title_short	An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
title_full	An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
title_fullStr	An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
title_full_unstemmed	An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
title_sort	implementation of regional descriptor and line roi in development of semi-automated strabismus detection system
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/10966/1/24p%20NUR%20SYAZLIN%20%20ZOLKIFLI.pdf http://eprints.uthm.edu.my/10966/2/NUR%20SYAZLIN%20%20ZOLKIFLI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10966/3/NUR%20SYAZLIN%20%20ZOLKIFLI%20WATERMARK.pdf
_version_	1804890126868807680

An implementation of regional descriptor and line roi in development of semi-automated strabismus detection system

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