Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation
Also available in printed version : QA248.5 M36 2013
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Master's thesis |
| اللغة: | الإنجليزية |
| منشور في: |
Universiti Teknologi Malaysia
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://utmik.utm.my/handle/123456789/115800 |
| Abstract | Abstract here |
| _version_ | 1854975096282677248 |
|---|---|
| author | Ismail Yaqub Maolood |
| author2 | Ghazali Sulong, supervisor |
| author_facet | Ghazali Sulong, supervisor Ismail Yaqub Maolood |
| author_sort | Ismail Yaqub Maolood |
| description | Also available in printed version : QA248.5 M36 2013 |
| format | Master's thesis |
| id | utm-123456789-115800 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-1158002025-08-21T08:22:48Z Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation Ismail Yaqub Maolood Ghazali Sulong, supervisor Fuzzy algorithms Magnetic resonance imaging Also available in printed version : QA248.5 M36 2013 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 zulaihi UTM 69 p. Thesis (Sarjana Sains (Sains Komputer)) - Universiti Teknologi Malaysia, 2014 2025-04-21T02:25:53Z 2025-04-21T02:25:53Z 2013 Master's thesis https://utmik.utm.my/handle/123456789/115800 valet-20151125-143858 vital:82473 ENG Closed Access UTM Complete Completion Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Fuzzy algorithms Magnetic resonance imaging Ismail Yaqub Maolood Fuzzy C-means clustering algorithm with level set for MRI cerebral tissue segmentation |
| thesis_level | Master |
| title | 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_short | 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 |
| topic | Fuzzy algorithms Magnetic resonance imaging |
| url | https://utmik.utm.my/handle/123456789/115800 |
| work_keys_str_mv | AT ismailyaqubmaolood fuzzycmeansclusteringalgorithmwithlevelsetformricerebraltissuesegmentation |