Improved fast fuzzy C-mean and its application in medical image segmentation
Image segmentation is a preliminary stage in diagnosis tools and the accurate segmentation of medical images is crucial for a correct diagnosis by these tools. Sometimes, due to inhomogeneity, low contrast, noise and inequality of content with semantic, automatic methods fail to segment image correc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing Company
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/15133/ http://psasir.upm.edu.my/id/eprint/15133/1/Improved%20fast%20fuzzy%20C-mean%20and%20its%20application%20in%20medical%20image%20segmentation.pdf |
| _version_ | 1848842591927271424 |
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| author | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mashohor, Syamsiah Mahmud, Rozi |
| author_facet | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mashohor, Syamsiah Mahmud, Rozi |
| author_sort | Balafar, Mohammad Ali |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Image segmentation is a preliminary stage in diagnosis tools and the accurate segmentation of medical images is crucial for a correct diagnosis by these tools. Sometimes, due to inhomogeneity, low contrast, noise and inequality of content with semantic, automatic methods fail to segment image correctly. Therefore, for these images, it is necessary to use user help to correct method's error. We proposed to upgrade FAST FCM method to use training data to have more accurate results. In this paper, instead of using pixels as training data which is usual, we used different gray levels as training data and that is why we have used FAST FCM, because the input of FAST FCM is gray levels exist in image (histogram of the image). We named the new clustering method improved fast fuzzy C-mean (FCM). We use two facts to improve fast FCM. First, training data for each class are the member of the class. Second, the relevance distance of each input data from the training data of a class show the distance of the input data from the class. To cluster an image, first, the color image is converted to gray level image; then, from histogram of image, user selects training data for each target class, afterwards, the image is clustered using postulated clustering method. Experimental result is demonstrated to show effectiveness of the new method. |
| first_indexed | 2025-11-15T08:01:34Z |
| format | Article |
| id | upm-15133 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:01:34Z |
| publishDate | 2010 |
| publisher | World Scientific Publishing Company |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-151332019-05-09T01:25:31Z http://psasir.upm.edu.my/id/eprint/15133/ Improved fast fuzzy C-mean and its application in medical image segmentation Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mashohor, Syamsiah Mahmud, Rozi Image segmentation is a preliminary stage in diagnosis tools and the accurate segmentation of medical images is crucial for a correct diagnosis by these tools. Sometimes, due to inhomogeneity, low contrast, noise and inequality of content with semantic, automatic methods fail to segment image correctly. Therefore, for these images, it is necessary to use user help to correct method's error. We proposed to upgrade FAST FCM method to use training data to have more accurate results. In this paper, instead of using pixels as training data which is usual, we used different gray levels as training data and that is why we have used FAST FCM, because the input of FAST FCM is gray levels exist in image (histogram of the image). We named the new clustering method improved fast fuzzy C-mean (FCM). We use two facts to improve fast FCM. First, training data for each class are the member of the class. Second, the relevance distance of each input data from the training data of a class show the distance of the input data from the class. To cluster an image, first, the color image is converted to gray level image; then, from histogram of image, user selects training data for each target class, afterwards, the image is clustered using postulated clustering method. Experimental result is demonstrated to show effectiveness of the new method. World Scientific Publishing Company 2010 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/15133/1/Improved%20fast%20fuzzy%20C-mean%20and%20its%20application%20in%20medical%20image%20segmentation.pdf Balafar, Mohammad Ali and Ramli, Abdul Rahman and Saripan, M. Iqbal and Mashohor, Syamsiah and Mahmud, Rozi (2010) Improved fast fuzzy C-mean and its application in medical image segmentation. Journal of Circuits, Systems and Computers, 19 (1). pp. 203-214. ISSN 0218-1266; ESSN: 1793-6454 https://www.worldscientific.com/doi/abs/10.1142/S0218126610006001 10.1142/S0218126610006001 |
| spellingShingle | Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mashohor, Syamsiah Mahmud, Rozi Improved fast fuzzy C-mean and its application in medical image segmentation |
| title | Improved fast fuzzy C-mean and its application in medical image segmentation |
| title_full | Improved fast fuzzy C-mean and its application in medical image segmentation |
| title_fullStr | Improved fast fuzzy C-mean and its application in medical image segmentation |
| title_full_unstemmed | Improved fast fuzzy C-mean and its application in medical image segmentation |
| title_short | Improved fast fuzzy C-mean and its application in medical image segmentation |
| title_sort | improved fast fuzzy c-mean and its application in medical image segmentation |
| url | http://psasir.upm.edu.my/id/eprint/15133/ http://psasir.upm.edu.my/id/eprint/15133/ http://psasir.upm.edu.my/id/eprint/15133/ http://psasir.upm.edu.my/id/eprint/15133/1/Improved%20fast%20fuzzy%20C-mean%20and%20its%20application%20in%20medical%20image%20segmentation.pdf |