Title :
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
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;
Conference_Titel :
Computer Graphics, Imaging and Visualisation, 2008. CGIV '08. Fifth International Conference on
Conference_Location :
Penang
Print_ISBN :
978-0-7695-3359-9
DOI :
10.1109/CGIV.2008.17
