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...
| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | 英語 |
| 出版事項: |
2018
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| 主題: | |
| オンライン･アクセス: | http://eprints.usm.my/44248/ |
| Abstract | Abstract here |
| _version_ | 1854968094876762112 |
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| author | Abdulbaqi, Hayder Saad |
| author_facet | Abdulbaqi, Hayder Saad |
| author_sort | Abdulbaqi, Hayder Saad |
| 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. |
| first_indexed | 2025-10-17T08:24:20Z |
| format | Thesis |
| id | usm-44248 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2025-10-17T08:24:20Z |
| publishDate | 2018 |
| record_format | eprints |
| record_pdf | Abstract |
| spelling | usm-442482019-05-03T07:33:04Z 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. |
| spellingShingle | QC1 Physics (General) Abdulbaqi, Hayder Saad HMRF Model For Brain Tumour Segmentation To Estimate The Volume Of MRI And CT Scan Images |
| title | 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_short | 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 |
| topic | QC1 Physics (General) |
| url | http://eprints.usm.my/44248/ |
| work_keys_str_mv | AT abdulbaqihaydersaad hmrfmodelforbraintumoursegmentationtoestimatethevolumeofmriandctscanimages |