The SEM statistical mixture model of segmentation algorithm of brain vessel image

Authors:
Xingce Wang;Feng Xu;Mingquan Zhou;Zhongke Wu;Xinyu Liu
Affiliations:
College of Information Science and Technology, Beijing Normal University, Beijing, China;College of Information Science and Technology, Beijing Normal University, Beijing, China;College of Information Science and Technology, Beijing Normal University, Beijing, China;College of Information Science and Technology, Beijing Normal University, Beijing, China;Institute of computing technology, Chinese Academy of Science, Beijing, China
Venue:
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Year:
2010

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Abstract

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.