Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)

Alzheimer's disease is becoming one of the most serious ailments that people face. Alzheimer's disease primarily affects those over the age of 65. is defined by the death of brain cells, which results in memory loss. as well as a lack of judgment, linguistic abilities, and decision-making...

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Main Author:	Gilani Mohamed, Mohamed Ahmed
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	R858-859.7 Computer applications to medicine Medical informatics
Online Access:	http://eprints.uthm.edu.my/6981/1/24p%20MOHAMED%20AHMED%20GILANI%20MOHAMED.pdf http://eprints.uthm.edu.my/6981/2/MOHAMED%20AHMED%20GILANI%20MOHAMED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6981/3/MOHAMED%20AHMED%20GILANI%20MOHAMED%20WATERMARK.pdf
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spelling	my-uthm-ep.69812022-04-24T00:49:04Z Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD) 2022-02 Gilani Mohamed, Mohamed Ahmed R858-859.7 Computer applications to medicine. Medical informatics Alzheimer's disease is becoming one of the most serious ailments that people face. Alzheimer's disease primarily affects those over the age of 65. is defined by the death of brain cells, which results in memory loss. as well as a lack of judgment, linguistic abilities, and decision-making capability Furthermore, no research has been conducted on developing a monitoring system for Alzheimer's disease that can continuously monitor Alzheimer's patients to identify any signs of development. Current research focuses mostly on early diagnosis and does not include disease monitoring. Monitoring is critical since it allows doctors to assess the disease development of Alzheimer's patients quantitatively. This study indicates developing an algorithm for detecting and progressing through the hippocampus of patients with Alzheimer's disease in MRI images. The active contour method (Chan-Vese) was utilized to extract the ROI parameters (hippocampus). The active contours algorithm deforms an item's initial border in an image to latch onto typical features inside the region of interest given an approximation of the object's perimeter. This is constantly stretched until it reaches the ROI's boundary. The interactive area selection approach is used to allow the user to determine the ROI depending on their needs. The algorithm will be applied once the ROI has been specified. The algorithm will be able to identify the parameters, such as the number of pixels, area pixels, and mean value, by extracting the hippocampal shape. The extraction of parameters will allow us to determine the extent of the patient's Alzheimer's progression. As a result, the study was successful in developing a semi-automated and robust model based on the Chan-Vese segmentation methodology, where it could observe the shrinking of the patient brain by the progression method using the total pixels of the hippocampus and its area by getting decreased at the second visit, one of the results showed at the first visit the total number of the pixels was 707 then at the second visit it shows 650 so the progression percentage 9%, and the proposed method produced promising segmentation results. In addition, a graphical user interface (GUI) was created to identify the progression percentage. As a future plan, this project can use machine learning to train the data for auto-detection for the hippocampus which will be significantly robust and more effective. 2022-02 Thesis http://eprints.uthm.edu.my/6981/ http://eprints.uthm.edu.my/6981/1/24p%20MOHAMED%20AHMED%20GILANI%20MOHAMED.pdf text en public http://eprints.uthm.edu.my/6981/2/MOHAMED%20AHMED%20GILANI%20MOHAMED%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6981/3/MOHAMED%20AHMED%20GILANI%20MOHAMED%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	R858-859.7 Computer applications to medicine Medical informatics
spellingShingle	R858-859.7 Computer applications to medicine Medical informatics Gilani Mohamed, Mohamed Ahmed Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
description	Alzheimer's disease is becoming one of the most serious ailments that people face. Alzheimer's disease primarily affects those over the age of 65. is defined by the death of brain cells, which results in memory loss. as well as a lack of judgment, linguistic abilities, and decision-making capability Furthermore, no research has been conducted on developing a monitoring system for Alzheimer's disease that can continuously monitor Alzheimer's patients to identify any signs of development. Current research focuses mostly on early diagnosis and does not include disease monitoring. Monitoring is critical since it allows doctors to assess the disease development of Alzheimer's patients quantitatively. This study indicates developing an algorithm for detecting and progressing through the hippocampus of patients with Alzheimer's disease in MRI images. The active contour method (Chan-Vese) was utilized to extract the ROI parameters (hippocampus). The active contours algorithm deforms an item's initial border in an image to latch onto typical features inside the region of interest given an approximation of the object's perimeter. This is constantly stretched until it reaches the ROI's boundary. The interactive area selection approach is used to allow the user to determine the ROI depending on their needs. The algorithm will be applied once the ROI has been specified. The algorithm will be able to identify the parameters, such as the number of pixels, area pixels, and mean value, by extracting the hippocampal shape. The extraction of parameters will allow us to determine the extent of the patient's Alzheimer's progression. As a result, the study was successful in developing a semi-automated and robust model based on the Chan-Vese segmentation methodology, where it could observe the shrinking of the patient brain by the progression method using the total pixels of the hippocampus and its area by getting decreased at the second visit, one of the results showed at the first visit the total number of the pixels was 707 then at the second visit it shows 650 so the progression percentage 9%, and the proposed method produced promising segmentation results. In addition, a graphical user interface (GUI) was created to identify the progression percentage. As a future plan, this project can use machine learning to train the data for auto-detection for the hippocampus which will be significantly robust and more effective.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Gilani Mohamed, Mohamed Ahmed
author_facet	Gilani Mohamed, Mohamed Ahmed
author_sort	Gilani Mohamed, Mohamed Ahmed
title	Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
title_short	Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
title_full	Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
title_fullStr	Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
title_full_unstemmed	Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)
title_sort	development of hippocampus mri image segmentation algorithm for progression detection of alzheimer’s disease (ad)
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/6981/1/24p%20MOHAMED%20AHMED%20GILANI%20MOHAMED.pdf http://eprints.uthm.edu.my/6981/2/MOHAMED%20AHMED%20GILANI%20MOHAMED%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6981/3/MOHAMED%20AHMED%20GILANI%20MOHAMED%20WATERMARK.pdf
_version_	1747831102612963328

Development of hippocampus MRI image segmentation algorithm for progression detection of alzheimer’s disease (AD)

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