Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation
FCM does not use spatial information in clustering process. Therefore, it is not robust against noise and other imaging artefacts. In order to incorporate spatial information, an extension for FCM (FCM_S) is proposed which allows pixel to be labelled by influence of its neighbourhood labels. FCM_S i...
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IEEE
2010
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Linkit: | http://psasir.upm.edu.my/id/eprint/68752/1/Compare%20different%20spatial%20based%20fuzzy-C_mean%20%28FCM%29%20extensions%20for%20MRI%20image%20segmentation.pdf |
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oai:psasir.upm.edu.my:68752 http://psasir.upm.edu.my/id/eprint/68752/ Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation Balafar, Mohammad Ali Ramli, Abdul Rahman Mashohor, Syamsiah Farzan, Ali FCM does not use spatial information in clustering process. Therefore, it is not robust against noise and other imaging artefacts. In order to incorporate spatial information, an extension for FCM (FCM_S) is proposed which allows pixel to be labelled by influence of its neighbourhood labels. FCM_S is time-consuming. To over come this problem, FCM_S1 is introduced, which is faster. Then, FCM_EN and FGFCM are proposed which are faster than previous methods. Four spatial based extensions are simulated for FCM: FCM_S, FCM_S1, FCM_EN and FGFCM. In order to compare their quality, they are applied to simulated brain MRI images and similarity index is used to compare their quality quantitatively. IEEE 2010 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/68752/1/Compare%20different%20spatial%20based%20fuzzy-C_mean%20%28FCM%29%20extensions%20for%20MRI%20image%20segmentation.pdf Balafar, Mohammad Ali and Ramli, Abdul Rahman and Mashohor, Syamsiah and Farzan, Ali (2010) Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation. In: 2nd International Conference on Computer and Automation Engineering (ICCAE 2010), 26-28 Feb. 2010, Singapore. (pp. 609-611). 10.1109/ICCAE.2010.5451302 |
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UPM IR |
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UPM IR |
language |
English |
description |
FCM does not use spatial information in clustering process. Therefore, it is not robust against noise and other imaging artefacts. In order to incorporate spatial information, an extension for FCM (FCM_S) is proposed which allows pixel to be labelled by influence of its neighbourhood labels. FCM_S is time-consuming. To over come this problem, FCM_S1 is introduced, which is faster. Then, FCM_EN and FGFCM are proposed which are faster than previous methods. Four spatial based extensions are simulated for FCM: FCM_S, FCM_S1, FCM_EN and FGFCM. In order to compare their quality, they are applied to simulated brain MRI images and similarity index is used to compare their quality quantitatively. |
format |
Conference or Workshop Item |
author |
Balafar, Mohammad Ali Ramli, Abdul Rahman Mashohor, Syamsiah Farzan, Ali |
spellingShingle |
Balafar, Mohammad Ali Ramli, Abdul Rahman Mashohor, Syamsiah Farzan, Ali Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation |
author_facet |
Balafar, Mohammad Ali Ramli, Abdul Rahman Mashohor, Syamsiah Farzan, Ali |
author_sort |
Balafar, Mohammad Ali |
title |
Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation |
title_short |
Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation |
title_full |
Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation |
title_fullStr |
Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation |
title_full_unstemmed |
Compare different spatial based fuzzy-C_mean (FCM) extensions for MRI image segmentation |
title_sort |
compare different spatial based fuzzy-c_mean (fcm) extensions for mri image segmentation |
publisher |
IEEE |
publishDate |
2010 |
url |
http://psasir.upm.edu.my/id/eprint/68752/1/Compare%20different%20spatial%20based%20fuzzy-C_mean%20%28FCM%29%20extensions%20for%20MRI%20image%20segmentation.pdf |
_version_ |
1819298725179162624 |
score |
13.4562235 |