高斯混合模型融合SSC的脑部MR图像分割

BRAIN MR IMAGE SEGMENTATION BASED ON FUSION OF GAUSSIAN MIXED MODEL AND SSC

针对脑补核磁共振图像分割问题,提出一种基于高斯混合模型融合半监督聚类方法。首先,从原始数据集随机选取MR图像切片;然后,利用k-均值算法聚类;最后,利用统计学和所得聚类的类标签信息完成分类。实验结果表明,该方法加快了收敛速度。相比两种有监督高斯混合模型,该方法取得了更好的分割结果,且无需标记训练数据集。

For the magnetic resonance brain image segmentation problem, we proposed a method which is based on fusing Gaussian mixed model and semi-supervised clustering （SSC）. First, we randomly selected MR image slices from original dataset. Then, we used k-mean algorithm for clustering. Finally, we employed statistics and the class label information of the derived clusters to complete the classification. Experimental results showed that the proposed method speeded up the convergence. Compared with two supervised Gaussian mixed models, the proposed method achieved better segmentation results and did not need labelled training dataset.