Affiliations: Faculty of Information Technology, Multimedia
University, Jalan Multimedia, 63100 Cyberjaya, Selangor, Malaysia. E-mail:
[email protected] | Faculty of Engineering, Multimedia University, Jalan
Multimedia, 63100 Cyberjaya, Selangor, Malaysia
Abstract: In this paper, we present non-identical unsupervised clustering
techniques for the segmentation of CT brain images. Prior to segmentation, we
enhance the visualization of the original image. Generally, for the presence of
abnormal regions in the brain images, we partition them into 3 segments, which
are the abnormal regions itself, the cerebrospinal fluid (CSF) and the brain
matter. However, for the absence of abnormal regions in the brain images, the
final segmented regions will consist of CSF and brain matter only. Therefore,
our system is divided into two stages of clustering. The initial clustering
technique is for the detection of the abnormal regions. The later clustering
technique is for the segmentation of the CSF and brain matter. The system has
been tested with a number of real CT head images and has achieved satisfactory
results.
