Cover Image

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...

Full description

Saved in:
Bibliographic Details
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
http://eprints.usm.my/44248/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.usm.eprints.44248
record_format eprints
spelling my.usm.eprints.44248 http://eprints.usm.my/44248/ HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images 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 NonPeerReviewed application/pdf en http://eprints.usm.my/44248/1/HAYDER%20SAAD%20ABDULBAQI.pdf Abdulbaqi, Hayder Saad (2018) HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
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
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
publishDate 2018
url http://eprints.usm.my/44248/1/HAYDER%20SAAD%20ABDULBAQI.pdf
http://eprints.usm.my/44248/
_version_ 1643710953354690560
score 13.160551

Similar Items