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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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 |