Proceedings of the Korean Society of Broadcast Engineers Conference (한국방송∙미디어공학회:학술대회논문집)
- 2009.01a
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- Pages.217-222
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- 2009
CT HEAD IMAGES SEGMENTATION USING UNSUPERVISED TECHNIQUES
- Lee, Tong Hau (Faculty of Information Technology, Multimedia University) ;
- Fauzi, Mohammad Faizal Ahmad (Faculty of Engineering, Multimedia University) ;
- Komiya, Ryoichi (Faculty of Information Technology, Multimedia University) ;
- Hu, Ng (Faculty of Information Technology, Multimedia University)
- Published : 2009.01.12
Abstract
In this paper, a new approach is proposed for the segmentation of Computed Tomography (CT) head images. The approach consists of two-stage segmentation with each stage contains two different segmentation techniques. The ultimate aim is to segment the CT head images into three classes which are abnormalities, cerebrospinal fluid (CSF) and brain matter. For the first stage segmentation, k-means and fuzzy c-means (FCM) segmentation are implemented in order to acquire the abnormalities. Whereas for the second stage segmentation, modified FCM with population-diameter independent (PDI) and expectation-maximization (EM) segmentation are adopted to obtain the CSF and brain matter. The experimental results have demonstrated that the proposed system is feasible and achieve satisfactory results.
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