MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm
Image segmentation is a critical stage in many computer vision and image process applications. Accurate segmentation of medical images is very essential in Medical applications but it is very difficult job due to noise and in homogeneity. Fuzzy C-Mean (FCM) is one of the most popular Medical image c...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/45049/ http://psasir.upm.edu.my/id/eprint/45049/1/MRI%20segmentation%20of%20medical%20images%20using%20FCM%20with%20initialized%20class%20centers%20via%20genetic%20algorithm.pdf |
| _version_ | 1848850474717937664 |
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| author | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah Balafar, Hakimeh |
| author_facet | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah Balafar, Hakimeh |
| author_sort | Balafar, Mohammad Ali |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Image segmentation is a critical stage in many computer vision and image process applications. Accurate segmentation of medical images is very essential in Medical applications but it is very difficult job due to noise and in homogeneity. Fuzzy C-Mean (FCM) is one of the most popular Medical image clustering methods. We noticed that for some images, FCM is sensitive to initialization of centre of clusters. This article introduced a new method based on the combination of genetic algorithm and FCM to solve this problem. The genetic algorithm is used to find initialized centre of the clusters. In this method, the centre is obtained by minimizing an object Function. This object Function specifies sum of distances between each data and their cluster centres. Then FCM is applied with to the case. The experimental result demonstrates the effectiveness of new method by able to initialize centre of the clusters. |
| first_indexed | 2025-11-15T10:06:52Z |
| format | Conference or Workshop Item |
| id | upm-45049 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:06:52Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-450492020-08-10T02:26:08Z http://psasir.upm.edu.my/id/eprint/45049/ MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah Balafar, Hakimeh Image segmentation is a critical stage in many computer vision and image process applications. Accurate segmentation of medical images is very essential in Medical applications but it is very difficult job due to noise and in homogeneity. Fuzzy C-Mean (FCM) is one of the most popular Medical image clustering methods. We noticed that for some images, FCM is sensitive to initialization of centre of clusters. This article introduced a new method based on the combination of genetic algorithm and FCM to solve this problem. The genetic algorithm is used to find initialized centre of the clusters. In this method, the centre is obtained by minimizing an object Function. This object Function specifies sum of distances between each data and their cluster centres. Then FCM is applied with to the case. The experimental result demonstrates the effectiveness of new method by able to initialize centre of the clusters. IEEE 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/45049/1/MRI%20segmentation%20of%20medical%20images%20using%20FCM%20with%20initialized%20class%20centers%20via%20genetic%20algorithm.pdf Balafar, Mohammad Ali and Ramli, Abdul Rahman and Saripan, M. Iqbal and Mahmud, Rozi and Mashohor, Syamsiah and Balafar, Hakimeh (2008) MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm. In: 3rd International Symposium on Information Technology (ITSim'08), 26-28 Aug. 2008, Kuala Lumpur, Malaysia. . 10.1109/ITSIM.2008.4631864 |
| spellingShingle | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah Balafar, Hakimeh MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| title | MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| title_full | MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| title_fullStr | MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| title_full_unstemmed | MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| title_short | MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm |
| title_sort | mri segmentation of medical images using fcm with initialized class centers via genetic algorithm |
| url | http://psasir.upm.edu.my/id/eprint/45049/ http://psasir.upm.edu.my/id/eprint/45049/ http://psasir.upm.edu.my/id/eprint/45049/1/MRI%20segmentation%20of%20medical%20images%20using%20FCM%20with%20initialized%20class%20centers%20via%20genetic%20algorithm.pdf |