MIP-Guided Blood Vessel Segmentation Using SEM Statistical Mixture Model

Abstract

Blood vessel segmentation is an essential step of the diagnoses of various brain diseases. In this paper, we propose a novel method for segmentation of cerebral blood vessels from magnetic resonance angiography (MRA) images based on Gaussian Mixture Model and the SEM algorithm. 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 other 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 and the accuracy of segmentation is improved by the random assortment iteration. Our method is tested on head MRA datasets, it is demonstrated to be efficient.