In this paper, a segmentation method using Gaussian Mixture Model (GMM) combined with template match is proposed for analysis of brain CT images. The specific aim of this method is to extract ventricles from brain CT images. These can then be used for automated detection of the midline shift in brain. In the method, different types of brain tissue, of which the ventricles form the region of interest, are segmented using multiple Gaussian mixtures. Expectation Maximization (EM) method is used to train the GMM. Ventriclular tissue is then detected in the segmented regions using template matching. Other segmentation methods, including K-means clustering and Iterated Conditional Modes (ICM), are also implemented and their results are compared with those of the proposed method. The algorithms are evaluated against a dataset of brain CT images captured from both normal and TBI cases. The segmentation results show the advantages of the proposed GMM-based method for brain tissue modeling. The computational complexity of the proposed method is also discussed, as well as the means to address this issue. The proposed GMM-based method allows accurate segmentation of ventricles required for detection of the shift in the midline.
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