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

Author

Tong Hau Lee ; Fauzi, Mohd F. A. ; Komiya, Ryoichi

Author_Institution

Fac. of Inf. Technol., Multimedia Univ., Cyberjaya

fYear

2008

fDate

26-28 Aug. 2008

Firstpage

339

Lastpage

344

Abstract

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 tree. 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.

Keywords

brain; computerised tomography; decision trees; expectation-maximisation algorithm; filtering theory; image segmentation; medical image processing; pattern clustering; CT brain image segmentation; K-means clustering; abnormal regions filtering; brain matter; cerebrospinal fluid; decision tree; expectation-maximization clustering; intracranial area; Biomedical imaging; Brain; Clustering algorithms; Computed tomography; Decision trees; Histograms; Image segmentation; Magnetic heads; Medical diagnostic imaging; Visualization; Computed tomography; Expectation-maximization; K-means; Segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Graphics, Imaging and Visualisation, 2008. CGIV '08. Fifth International Conference on

Conference_Location

Penang

Print_ISBN

978-0-7695-3359-9

Type

conf

DOI

10.1109/CGIV.2008.17

Filename

4627028