Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image
Image segmentation is a critical part of clinical diagnostic tools. Medical images mostly contain noise. Therefore, accurate segmentation of medical images is highly challenging; however, accurate segmentation of these images is very important in correct diagnosis by clinical tools. We proposed a ne...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IET
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/68230/ http://psasir.upm.edu.my/id/eprint/68230/1/Medical%20image%20segmentation%20using%20fuzzy%20c-mean%20%28FCM%29%20and%20dominant%20grey%20levels%20of%20image.pdf |
| _version_ | 1848856068877189120 |
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| author | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah |
| author_facet | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah |
| author_sort | Balafar, Mohammad Ali |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Image segmentation is a critical part of clinical diagnostic tools. Medical images mostly contain noise. Therefore, accurate segmentation of medical images is highly challenging; however, accurate segmentation of these images is very important in correct diagnosis by clinical tools. We proposed a new method for image segmentation based on dominant grey level of image and Fuzzy C-Mean (FCM). In the postulated method, the colour image is converted to grey level image and stationary wavelet is applied to decrease noise; the image is clustered using ordinary FCM, afterwards, clusters with error more than a threshold are divided to two sub clusters. This process continues until there remain no such, erroneous, clusters. The dominant connected component of each cluster is obtained -- if existed. In obtained dominant connected components, the n biggest connected components are selected. N is specified based upon considered number of clusters. Averages of grey levels of n selected components, in grey level image, are considered as Dominant grey levels. Dominant grey levels are used as cluster centres. Eventually, the image is clustered using specified cluster centres. Experimental results are demonstrated to show effectiveness of new method. |
| first_indexed | 2025-11-15T11:35:47Z |
| format | Conference or Workshop Item |
| id | upm-68230 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:35:47Z |
| publishDate | 2008 |
| publisher | IET |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-682302019-05-10T08:25:30Z http://psasir.upm.edu.my/id/eprint/68230/ Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah Image segmentation is a critical part of clinical diagnostic tools. Medical images mostly contain noise. Therefore, accurate segmentation of medical images is highly challenging; however, accurate segmentation of these images is very important in correct diagnosis by clinical tools. We proposed a new method for image segmentation based on dominant grey level of image and Fuzzy C-Mean (FCM). In the postulated method, the colour image is converted to grey level image and stationary wavelet is applied to decrease noise; the image is clustered using ordinary FCM, afterwards, clusters with error more than a threshold are divided to two sub clusters. This process continues until there remain no such, erroneous, clusters. The dominant connected component of each cluster is obtained -- if existed. In obtained dominant connected components, the n biggest connected components are selected. N is specified based upon considered number of clusters. Averages of grey levels of n selected components, in grey level image, are considered as Dominant grey levels. Dominant grey levels are used as cluster centres. Eventually, the image is clustered using specified cluster centres. Experimental results are demonstrated to show effectiveness of new method. IET 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68230/1/Medical%20image%20segmentation%20using%20fuzzy%20c-mean%20%28FCM%29%20and%20dominant%20grey%20levels%20of%20image.pdf Balafar, Mohammad Ali and Ramli, Abdul Rahman and Saripan, M. Iqbal and Mahmud, Rozi and Mashohor, Syamsiah (2008) Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image. In: 2008 5th International Conference on Visual Information Engineering (VIE 2008), 29 July-1 Aug. 2008, Xi'an, China. (pp. 314-317). 10.1049/cp:20080329 |
| spellingShingle | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mahmud, Rozi Mashohor, Syamsiah Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image |
| title | Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image |
| title_full | Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image |
| title_fullStr | Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image |
| title_full_unstemmed | Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image |
| title_short | Medical image segmentation using fuzzy c-mean (FCM) and dominant grey levels of image |
| title_sort | medical image segmentation using fuzzy c-mean (fcm) and dominant grey levels of image |
| url | http://psasir.upm.edu.my/id/eprint/68230/ http://psasir.upm.edu.my/id/eprint/68230/ http://psasir.upm.edu.my/id/eprint/68230/1/Medical%20image%20segmentation%20using%20fuzzy%20c-mean%20%28FCM%29%20and%20dominant%20grey%20levels%20of%20image.pdf |