Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data

Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and inhomogeneity. Therefore, accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important and...

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Main Authors: Balafar, Mohammad Ali, Ramli, Abdul Rahman, Saripan, M. Iqbal, Mashohor, Syamsiah, Mahmud, Rozi
Format: Article
Language:English
Published: World Scientific Publishing Company 2010
Online Access:http://psasir.upm.edu.my/id/eprint/15595/1/Medical%20image%20segmentation%20using%20Fuzzy%20C.pdf
http://psasir.upm.edu.my/id/eprint/15595/
https://www.worldscientific.com/doi/abs/10.1142/S0218126610005913
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Institution: Universiti Putra Malaysia
Language: English
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spelling my.upm.eprints.155952019-04-10T06:30:59Z http://psasir.upm.edu.my/id/eprint/15595/ Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data Balafar, Mohammad Ali Ramli, Abdul Rahman Saripan, M. Iqbal Mashohor, Syamsiah Mahmud, Rozi Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and inhomogeneity. Therefore, accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We proposed a new clustering method based on Fuzzy C-Mean (FCM) and user specified data. In the postulated method, the color image is converted to grey level image and anisotropic filter is applied to decrease noise; User selects training data for each target class, afterwards, the image is clustered using ordinary FCM. Due to inhomogeneity and unknown noise some clusters contain training data for more than one target class. These clusters are partitioned again. This process continues until there are no such clusters. Then, the clusters contain training data for a target class assigned to that target class; mean of intensity in each class is considered as feature for that class, afterwards, feature distance of each unsigned cluster from different class is found then unsigned clusters are signed to target class with least distance from. Experimental result is demonstrated to show effectiveness of new method. World Scientific Publishing Company 2010-02 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/15595/1/Medical%20image%20segmentation%20using%20Fuzzy%20C.pdf Balafar, Mohammad Ali and Ramli, Abdul Rahman and Saripan, M. Iqbal and Mashohor, Syamsiah and Mahmud, Rozi (2010) Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data. Journal of Circuits, Systems and Computers, 19 (1). pp. 1-14. ISSN 0218-1266; ESSN: 1793-6454 https://www.worldscientific.com/doi/abs/10.1142/S0218126610005913 10.1142/S0218126610005913
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Image segmentation is one of the most important parts of clinical diagnostic tools. Medical images mostly contain noise and inhomogeneity. Therefore, accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We proposed a new clustering method based on Fuzzy C-Mean (FCM) and user specified data. In the postulated method, the color image is converted to grey level image and anisotropic filter is applied to decrease noise; User selects training data for each target class, afterwards, the image is clustered using ordinary FCM. Due to inhomogeneity and unknown noise some clusters contain training data for more than one target class. These clusters are partitioned again. This process continues until there are no such clusters. Then, the clusters contain training data for a target class assigned to that target class; mean of intensity in each class is considered as feature for that class, afterwards, feature distance of each unsigned cluster from different class is found then unsigned clusters are signed to target class with least distance from. Experimental result is demonstrated to show effectiveness of new method.
format Article
author Balafar, Mohammad Ali
Ramli, Abdul Rahman
Saripan, M. Iqbal
Mashohor, Syamsiah
Mahmud, Rozi
spellingShingle Balafar, Mohammad Ali
Ramli, Abdul Rahman
Saripan, M. Iqbal
Mashohor, Syamsiah
Mahmud, Rozi
Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data
author_facet Balafar, Mohammad Ali
Ramli, Abdul Rahman
Saripan, M. Iqbal
Mashohor, Syamsiah
Mahmud, Rozi
author_sort Balafar, Mohammad Ali
title Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data
title_short Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data
title_full Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data
title_fullStr Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data
title_full_unstemmed Medical image segmentation using Fuzzy C-Mean (FCM) and user specified data
title_sort medical image segmentation using fuzzy c-mean (fcm) and user specified data
publisher World Scientific Publishing Company
publishDate 2010
url http://psasir.upm.edu.my/id/eprint/15595/1/Medical%20image%20segmentation%20using%20Fuzzy%20C.pdf
http://psasir.upm.edu.my/id/eprint/15595/
https://www.worldscientific.com/doi/abs/10.1142/S0218126610005913
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