Segmentation algorithm of brain vessel image based on SEM statistical mixture model

Medical image segmentation has been a hot spot in recent years. And the segmentation of brain vessels image becomes a key-problem due to its complicated structure and small proportion. In this paper, the brain MRI images are processed with statistical analysis technology, and then the accuracy of segmentation is improved by the random assortment iteration .First the MIP algorithm is applied to decrease the quantity of mixing elements. Then the Gaussian Mixture Model is put forward to fit the stochastic distribution of the brain vessels and brain tissue. Finally, the SEM algorithm is adopted to estimate the parameters of Gaussian Mixture Model. The feasibility and validity of the model is verified by the experiment. With the model, small branches of the brain vessel can be segmented, the speed of the convergent is improved and local minima are avoided.