Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation
The brain is the most complex organ in the human body, and it consists of four regions namely, gray matter, white matter, cerebrospinal fluid and background. It is widely accepted as an imaging modality for detecting a variety of conditions of the brain such as tumours, bleeding, swelling, infection...
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2013
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my-utm-ep.418502020-07-07T01:23:44Z Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation 2013-11 Maolood, Ismail Yaqub QA Mathematics The brain is the most complex organ in the human body, and it consists of four regions namely, gray matter, white matter, cerebrospinal fluid and background. It is widely accepted as an imaging modality for detecting a variety of conditions of the brain such as tumours, bleeding, swelling, infections, or problems associated with blood vessels. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. This thesis presents a new approach of Magnetic Resonance Imaging (MRI) brain tissue segmentation, which consists of three main phases: (1) Noise removal using median filter, (2) Tissue clustering based on the fuzzy c-means, and (3) Tissue segmentation using the fuzzy level set method, which finally separates white matter from gray matter. The results show that the segmentation’s accuracy rates of 98% is achieved when tested on 100 samples of MRI brain images atlas dataset. 2013-11 Thesis http://eprints.utm.my/id/eprint/41850/ http://eprints.utm.my/id/eprint/41850/5/IsmailYaqubNaoloodMFSKSM2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:82473 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing |
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Universiti Teknologi Malaysia |
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UTM Institutional Repository |
| language |
English |
| topic |
QA Mathematics |
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QA Mathematics Maolood, Ismail Yaqub Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| description |
The brain is the most complex organ in the human body, and it consists of four regions namely, gray matter, white matter, cerebrospinal fluid and background. It is widely accepted as an imaging modality for detecting a variety of conditions of the brain such as tumours, bleeding, swelling, infections, or problems associated with blood vessels. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. This thesis presents a new approach of Magnetic Resonance Imaging (MRI) brain tissue segmentation, which consists of three main phases: (1) Noise removal using median filter, (2) Tissue clustering based on the fuzzy c-means, and (3) Tissue segmentation using the fuzzy level set method, which finally separates white matter from gray matter. The results show that the segmentation’s accuracy rates of 98% is achieved when tested on 100 samples of MRI brain images atlas dataset. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Maolood, Ismail Yaqub |
| author_facet |
Maolood, Ismail Yaqub |
| author_sort |
Maolood, Ismail Yaqub |
| title |
Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| title_short |
Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| title_full |
Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| title_fullStr |
Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| title_full_unstemmed |
Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| title_sort |
fuzzy c-means clustering algorithm with level set for mri cerebral tissue segmentation |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Computing |
| granting_department |
Faculty of Computing |
| publishDate |
2013 |
| url |
http://eprints.utm.my/id/eprint/41850/5/IsmailYaqubNaoloodMFSKSM2013.pdf |
| _version_ |
1747816632024039424 |