Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering

Manual segmentation infarct core of acute ischemic stroke from medical imaging currently faces a few challenges and causes a high intra- and inter-observer difference. Besides the present standard is tedious and time taking task performed by the radiologists, and the outcome is depending on their ex...

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Main Author:	Abang Mohd Arif Anaqi, Abang Isa
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://ir.unimas.my/id/eprint/36627/3/Abang%20Mohd%20Arif.pdf
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id	my-unimas-ir.36627
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spelling	my-unimas-ir.366272023-11-10T03:12:30Z Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering 2021-04-30 Abang Mohd Arif Anaqi, Abang Isa TK Electrical engineering. Electronics Nuclear engineering Manual segmentation infarct core of acute ischemic stroke from medical imaging currently faces a few challenges and causes a high intra- and inter-observer difference. Besides the present standard is tedious and time taking task performed by the radiologists, and the outcome is depending on their experience. Research has shown that an automated segmentation from the Magnetic Resonance Image (MRI) is potentially giving more effective and accurate results. This study aims to develop an automatic segmentation by utilizing clustering algorithm for acute ischemic stroke lesion identification. Developing an automatic segmentation, the question remains: To what extent does an automatic segmentation give accurate results in segmenting the acute ischemic stroke region from the medical images, particularly in MRI image? Based on the thorough review of the literature on the automated segmentation of acute ischemic stroke, it can conclude that automatic segmentation consists of image acquisition, pre-processing, segmentation, and validation of the segmented image from the MRI. The result in this work shows the potential of the automated segmentation can distinguish between the healthy and affected brain tissue by high as 90.08% in accuracy and 0.89 in the dice coefficient. The development of an automatic segmentation algorithm was successfully achieved by entirely depending on the computer without human interaction. Further research is needed to identify other factors that could increase the effectiveness of automated segmentation. Universiti Malaysia Sarawak (UNIMAS) 2021-04 Thesis http://ir.unimas.my/id/eprint/36627/ http://ir.unimas.my/id/eprint/36627/3/Abang%20Mohd%20Arif.pdf text en validuser masters Universiti Malaysia Sarawak Faculty of Engineering
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Abang Mohd Arif Anaqi, Abang Isa Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
description	Manual segmentation infarct core of acute ischemic stroke from medical imaging currently faces a few challenges and causes a high intra- and inter-observer difference. Besides the present standard is tedious and time taking task performed by the radiologists, and the outcome is depending on their experience. Research has shown that an automated segmentation from the Magnetic Resonance Image (MRI) is potentially giving more effective and accurate results. This study aims to develop an automatic segmentation by utilizing clustering algorithm for acute ischemic stroke lesion identification. Developing an automatic segmentation, the question remains: To what extent does an automatic segmentation give accurate results in segmenting the acute ischemic stroke region from the medical images, particularly in MRI image? Based on the thorough review of the literature on the automated segmentation of acute ischemic stroke, it can conclude that automatic segmentation consists of image acquisition, pre-processing, segmentation, and validation of the segmented image from the MRI. The result in this work shows the potential of the automated segmentation can distinguish between the healthy and affected brain tissue by high as 90.08% in accuracy and 0.89 in the dice coefficient. The development of an automatic segmentation algorithm was successfully achieved by entirely depending on the computer without human interaction. Further research is needed to identify other factors that could increase the effectiveness of automated segmentation.
format	Thesis
qualification_level	Master's degree
author	Abang Mohd Arif Anaqi, Abang Isa
author_facet	Abang Mohd Arif Anaqi, Abang Isa
author_sort	Abang Mohd Arif Anaqi, Abang Isa
title	Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
title_short	Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
title_full	Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
title_fullStr	Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
title_full_unstemmed	Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering
title_sort	development of acute stroke lesion segmentation algorithm in brain mri using pseudo-colour with k-means clustering
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Engineering
publishDate	2021
url	http://ir.unimas.my/id/eprint/36627/3/Abang%20Mohd%20Arif.pdf
_version_	1783728478832558080

Development of Acute Stroke Lesion Segmentation Algorithm in Brain MRI using Pseudo-colour with K-means Clustering

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