Segmentation of CT Brain Images Using K-Means and EM Clustering

The combination of the different approaches for the segmentation of brain images is presented in this paper. The system segments the CT head images into 3 clusters, which are abnormal regions, cerebrospinal fluid (CSF) and brain matter. Firstly we filter out the abnormal regions from the intracrania...

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Main Authors: Lee, Tong Hau, Fauzi, Mohammad Faizal Ahmad, Komiya, Ryoichi
Format: Conference or Workshop Item
Published: 2008
Subjects:
Online Access:http://shdl.mmu.edu.my/2885/
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author Lee, Tong Hau
Fauzi, Mohammad Faizal Ahmad
Komiya, Ryoichi
author_facet Lee, Tong Hau
Fauzi, Mohammad Faizal Ahmad
Komiya, Ryoichi
author_sort Lee, Tong Hau
building MMU Institutional Repository
collection Online Access
description The combination of the different approaches for the segmentation of brain images is presented in this paper. The system segments the CT head images into 3 clusters, which are abnormal regions, cerebrospinal fluid (CSF) and brain matter. Firstly we filter out the abnormal regions from the intracranial area by using the decision free. As for the segmentation of the CSF and brain matter, we employed the Expectation-maximization (EM) algorithm. The system has been tested with a number of real CT head images and has achieved some promising results.
first_indexed 2025-11-14T18:08:27Z
format Conference or Workshop Item
id mmu-2885
institution Multimedia University
institution_category Local University
last_indexed 2025-11-14T18:08:27Z
publishDate 2008
recordtype eprints
repository_type Digital Repository
spelling mmu-28852011-09-21T07:25:27Z http://shdl.mmu.edu.my/2885/ Segmentation of CT Brain Images Using K-Means and EM Clustering Lee, Tong Hau Fauzi, Mohammad Faizal Ahmad Komiya, Ryoichi T Technology (General) QA75.5-76.95 Electronic computers. Computer science The combination of the different approaches for the segmentation of brain images is presented in this paper. The system segments the CT head images into 3 clusters, which are abnormal regions, cerebrospinal fluid (CSF) and brain matter. Firstly we filter out the abnormal regions from the intracranial area by using the decision free. As for the segmentation of the CSF and brain matter, we employed the Expectation-maximization (EM) algorithm. The system has been tested with a number of real CT head images and has achieved some promising results. 2008-08 Conference or Workshop Item NonPeerReviewed Lee, Tong Hau and Fauzi, Mohammad Faizal Ahmad and Komiya, Ryoichi (2008) Segmentation of CT Brain Images Using K-Means and EM Clustering. In: 5th International Conference on Computer Graphics, Imaging and Visualization (CGIV), 26-28 AUG 2008, Penang, MALAYSIA. http://dx.doi.org/10.1109/CGIV.2008.17 doi:10.1109/CGIV.2008.17 doi:10.1109/CGIV.2008.17
spellingShingle T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
Lee, Tong Hau
Fauzi, Mohammad Faizal Ahmad
Komiya, Ryoichi
Segmentation of CT Brain Images Using K-Means and EM Clustering
title Segmentation of CT Brain Images Using K-Means and EM Clustering
title_full Segmentation of CT Brain Images Using K-Means and EM Clustering
title_fullStr Segmentation of CT Brain Images Using K-Means and EM Clustering
title_full_unstemmed Segmentation of CT Brain Images Using K-Means and EM Clustering
title_short Segmentation of CT Brain Images Using K-Means and EM Clustering
title_sort segmentation of ct brain images using k-means and em clustering
topic T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/2885/
http://shdl.mmu.edu.my/2885/
http://shdl.mmu.edu.my/2885/