HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images

Magnetic resonance imaging (MRI) and computed tomography (CT) are two of the most important imaging technologies that enable the doctors to gain a reliable segmentation and estimation of brain tumours. The current study aims to develop a diagnostic method for medical images (MRI and CT) to achieve a...

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Main Author:	Abdulbaqi, Hayder Saad
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QC1 Physics (General)
Online Access:	http://eprints.usm.my/44248/1/HAYDER%20SAAD%20ABDULBAQI.pdf
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id	my-usm-ep.44248
record_format	uketd_dc
spelling	my-usm-ep.442482019-05-03T07:33:04Z HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images 2018-03 Abdulbaqi, Hayder Saad QC1 Physics (General) Magnetic resonance imaging (MRI) and computed tomography (CT) are two of the most important imaging technologies that enable the doctors to gain a reliable segmentation and estimation of brain tumours. The current study aims to develop a diagnostic method for medical images (MRI and CT) to achieve an accurate and precise segmentation and estimation of brain tumours. The proposed method in the study was applied on datasets had been collected from the Cancer Imaging Archive (TCIA) and Iraqi hospitals. The proposed method based on adapting and developing hidden Markov random field (HMRF) model and threshold method to carry out the segmentation of the brain tumours. 2018-03 Thesis http://eprints.usm.my/44248/ http://eprints.usm.my/44248/1/HAYDER%20SAAD%20ABDULBAQI.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Fizik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QC1 Physics (General)
spellingShingle	QC1 Physics (General) Abdulbaqi, Hayder Saad HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
description	Magnetic resonance imaging (MRI) and computed tomography (CT) are two of the most important imaging technologies that enable the doctors to gain a reliable segmentation and estimation of brain tumours. The current study aims to develop a diagnostic method for medical images (MRI and CT) to achieve an accurate and precise segmentation and estimation of brain tumours. The proposed method in the study was applied on datasets had been collected from the Cancer Imaging Archive (TCIA) and Iraqi hospitals. The proposed method based on adapting and developing hidden Markov random field (HMRF) model and threshold method to carry out the segmentation of the brain tumours.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abdulbaqi, Hayder Saad
author_facet	Abdulbaqi, Hayder Saad
author_sort	Abdulbaqi, Hayder Saad
title	HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
title_short	HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
title_full	HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
title_fullStr	HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
title_full_unstemmed	HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images
title_sort	hmrf model for brain tumour segmentation to estimate the volume of mri and ct scan images
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Fizik
publishDate	2018
url	http://eprints.usm.my/44248/1/HAYDER%20SAAD%20ABDULBAQI.pdf
_version_	1747821346778251264

HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images

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