Brain structure segmentation of Magnetic Resonance imaging using t-mixture algorithm

Medical image segmentation has become an essential technique in clinical and research-oriented applications. Because manual segmentation methods are tedious, and fully automatic segmentation lacks the flexibility of human intervention or correction, semiautomatic methods have become the preferred type of medical image segmentation. As Magnetic Resonance Imaging (MRI) is an important technology of radiological evaluation and computer-aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. The paper concerns medical image segmentation based on t-mixture model because of merits of the model. By analyzing the features of MR images, the main procedure of white matter segmentation of brain MR Images based on t-mixture model is outlined follows. The parameters of t-mixture model for the image are firstly estimated. Then the posterior probabilities of the pixels of the image are computed. At last, the image is segmented according to the Bayes decision rule for minimum error. Experimental results show that t-mixture model fits for medical image segmentation up to 400 iterations.