关键词: 高斯混合模型, EM算法, 粒子群算法, 图像分割, 核磁共振成像

Abstract: Segmenting brain from non-brain tissue，also know as skull stripping，has become an important image processing step in analyses involving image registration or cortical flattening.But with the effect of the bias fields，weak edges，strong noise，the level set method，only uses the information of the edges，can not get well results.Gaussian mixture model uses the global information of the image，so it can deal with the problems of the weak edges.But the traditional Gaussian mixture model only uses the information of the histogram and does not take the spatial information into account，so the model can not do well with noisy images.This paper constructs a new information field.Based on the new field information the Gaussian mixture model can reduce the effect of the bias field，noise，and prevent the noise covering the weak edges effectively.Gaussian mixture model can be solved by EM method，which can only get the local best results.In order to get the global results，particle swarm optimizer is introduced and improved to get result exactly and quickly.Experiments on the segmentation of brain magnetic resonance images show this model has better effect in image segmentation.

Key words: Gaussian mixture model, Expection-Maximization（EM） algorithm, particle swarm optimizer, image segmentation, nuclear Magnetic Resonance Imaging（MRI）