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Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff

Glaucoma is a disease that is defined by the pressure increased with the eyeball, causing severe damage to the optic nerve. One of the symptoms in glaucoma disease detection is an increased fluid pressure, which in the long term will damage the eye's optic nerve and it may in the worst case lea...

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Main Author: Md Ariff, Norharyati
Format: Thesis
Language:English
Published: 2016
Online Access:https://ir.uitm.edu.my/id/eprint/17874/2/17874.pdf
https://ir.uitm.edu.my/id/eprint/17874/
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spelling my.uitm.ir.178742023-09-07T06:52:18Z https://ir.uitm.edu.my/id/eprint/17874/ Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff Md Ariff, Norharyati Glaucoma is a disease that is defined by the pressure increased with the eyeball, causing severe damage to the optic nerve. One of the symptoms in glaucoma disease detection is an increased fluid pressure, which in the long term will damage the eye's optic nerve and it may in the worst case lead to blindness. Blindness due to optic nerve damage is irreversible unless it is intervened with proper treatment. In view of this, eye screening is important for early detection. Currently, the very important indicator for accessing the progression of glaucoma is the cup-to-disc ratio (CDR). Due to the complexity of Cup to Disc Ratio (CDR) measurement where the visibility of the boundary between optic cup and optic disc with high density vascular in the optic region, this research explores the methods that can detect the optic cup and optic disc by using digital fundus image as a cheaper solution for an eye screening. Image processing techniques were employed to segment and extract the optic cup and optic disc for glaucoma detection purpose. This study performed using a new bio-inspired algorithm; Selfish Gene Algorithm (SFGA) for optic cup and optic disc segmentation. In addition, this new algorithm is compared to color channel multi-thresholding segmentation and artificial intelligence segmentation based clustering method such as Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy cMeans (FCM). From the results and analysis obtained from this research, it is established that improved SFGA is outperformed ANFIS, FCM, and Color Channel Multi-thresholding. Therefore, SFGA has potential to greatly improve outcomes for the current technology. 2016 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/17874/2/17874.pdf Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff. (2016) Masters thesis, thesis, Universiti Teknologi MARA. <http://terminalib.uitm.edu.my/17874.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
description Glaucoma is a disease that is defined by the pressure increased with the eyeball, causing severe damage to the optic nerve. One of the symptoms in glaucoma disease detection is an increased fluid pressure, which in the long term will damage the eye's optic nerve and it may in the worst case lead to blindness. Blindness due to optic nerve damage is irreversible unless it is intervened with proper treatment. In view of this, eye screening is important for early detection. Currently, the very important indicator for accessing the progression of glaucoma is the cup-to-disc ratio (CDR). Due to the complexity of Cup to Disc Ratio (CDR) measurement where the visibility of the boundary between optic cup and optic disc with high density vascular in the optic region, this research explores the methods that can detect the optic cup and optic disc by using digital fundus image as a cheaper solution for an eye screening. Image processing techniques were employed to segment and extract the optic cup and optic disc for glaucoma detection purpose. This study performed using a new bio-inspired algorithm; Selfish Gene Algorithm (SFGA) for optic cup and optic disc segmentation. In addition, this new algorithm is compared to color channel multi-thresholding segmentation and artificial intelligence segmentation based clustering method such as Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy cMeans (FCM). From the results and analysis obtained from this research, it is established that improved SFGA is outperformed ANFIS, FCM, and Color Channel Multi-thresholding. Therefore, SFGA has potential to greatly improve outcomes for the current technology.
format Thesis
author Md Ariff, Norharyati
spellingShingle Md Ariff, Norharyati
Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff
author_facet Md Ariff, Norharyati
author_sort Md Ariff, Norharyati
title Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff
title_short Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff
title_full Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff
title_fullStr Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff
title_full_unstemmed Optic cup and optic disc segmentation using improved selfish gene algorithm / Norharyati Md Ariff
title_sort optic cup and optic disc segmentation using improved selfish gene algorithm / norharyati md ariff
publishDate 2016
url https://ir.uitm.edu.my/id/eprint/17874/2/17874.pdf
https://ir.uitm.edu.my/id/eprint/17874/
_version_ 1778165840552656896
score 13.211869

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