基于各向异性Gibbs随机场与高斯混合模型的脑MR图像分割算法
Brian MR Images Segmentation Based on Anisotropic Gibbs Random Field and Gauss Mixture Model
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摘要: 为了克服高斯混合模型(GMM)的局限性,利用Gibbs理论和图像结构信息构造各向异性Gibbs随机场,并将其引入到GMM框架中,完善GMM的分类效果,使其在克服噪声影响的同时,还能够保持细长拓扑结构区域信息以及角点区域信息.实验结果证明,文中算法可以得到较好的分类结果.Abstract: In order to overcome the limitation of Gauss mixture model (GMM),this article uses the Gibbs theory and the image structure information to construct anisotropic Gibbs random field incorporated into the GMM.The new GMM can reduce the effect of the noise and contain the information of beam structure regions and corner regions.Experiments on the segmentation of brain magnetic resonance images show that better effect in image segmentation can be achieved by the model.
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