Diagnosing Affected Organs Using Automated Iridology System

Iridology is the study of the iris of the eye for medical purposes. It is a preventive medicine since it can warn a person's tendency towards an apparent disease. Cleansing and healing of the body can be verified from changes in the iris. This study aims to design and develop an iris recognitio...

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Main Author:	Albusaidi, Hilal Nasser
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/1643/1/Hilal_Nasser_Albusaidi.pdf https://etd.uum.edu.my/1643/2/1.Hilal_Nasser_Albusaidi.pdf
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id	my-uum-etd.1643
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Albusaidi, Hilal Nasser Diagnosing Affected Organs Using Automated Iridology System
description	Iridology is the study of the iris of the eye for medical purposes. It is a preventive medicine since it can warn a person's tendency towards an apparent disease. Cleansing and healing of the body can be verified from changes in the iris. This study aims to design and develop an iris recognition system for automating iridology. The project involves 3 main steps: applying image processing techniques on eye image for data acquisition, collecting the database which is necessary for the iridology analysis and recognizing the affected organ in the body through iris analysis by using neural networks techniques. The image processing techniques are utilized for extracting eye images. A chart of right and left eyes has been acquired through the Internet and approved by an iridologist: Then, the extracted iris image is compared to the chart to determine the affected organ. Neural network with Back propagation is used to match the iris images with affected organ. A total of 159 images retrieved from internet was preprocessed and fed into NN engine. The Backprobagation network succeeded and getting best results because it attained to 96.2 % correction percentage.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Albusaidi, Hilal Nasser
author_facet	Albusaidi, Hilal Nasser
author_sort	Albusaidi, Hilal Nasser
title	Diagnosing Affected Organs Using Automated Iridology System
title_short	Diagnosing Affected Organs Using Automated Iridology System
title_full	Diagnosing Affected Organs Using Automated Iridology System
title_fullStr	Diagnosing Affected Organs Using Automated Iridology System
title_full_unstemmed	Diagnosing Affected Organs Using Automated Iridology System
title_sort	diagnosing affected organs using automated iridology system
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/1643/1/Hilal_Nasser_Albusaidi.pdf https://etd.uum.edu.my/1643/2/1.Hilal_Nasser_Albusaidi.pdf
_version_	1747827181636026368
spelling	my-uum-etd.16432022-04-21T03:15:57Z Diagnosing Affected Organs Using Automated Iridology System 2009-05 Albusaidi, Hilal Nasser College of Arts and Sciences (CAS) College of Arts and Sciences QA71-90 Instruments and machines Iridology is the study of the iris of the eye for medical purposes. It is a preventive medicine since it can warn a person's tendency towards an apparent disease. Cleansing and healing of the body can be verified from changes in the iris. This study aims to design and develop an iris recognition system for automating iridology. The project involves 3 main steps: applying image processing techniques on eye image for data acquisition, collecting the database which is necessary for the iridology analysis and recognizing the affected organ in the body through iris analysis by using neural networks techniques. The image processing techniques are utilized for extracting eye images. A chart of right and left eyes has been acquired through the Internet and approved by an iridologist: Then, the extracted iris image is compared to the chart to determine the affected organ. Neural network with Back propagation is used to match the iris images with affected organ. A total of 159 images retrieved from internet was preprocessed and fed into NN engine. The Backprobagation network succeeded and getting best results because it attained to 96.2 % correction percentage. 2009-05 Thesis https://etd.uum.edu.my/1643/ https://etd.uum.edu.my/1643/1/Hilal_Nasser_Albusaidi.pdf text eng public https://etd.uum.edu.my/1643/2/1.Hilal_Nasser_Albusaidi.pdf text eng public masters masters Universiti Utara Malaysia Abhyankar, A., Hornak, L. & Schuckers, S. (2005). Off-angle iris recognition using biorthogonal wavelet network system, Fourth IEEE Workshop on Automatic Identification Advanced Technologies 2005. 17-18 Oct. 2005, Page(s):239 - 244 Abiantun, R., Savvides, M. & Khosla, P. (2005). 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Diagnosing Affected Organs Using Automated Iridology System

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