基于K-means和图割的脑部MRI分割算法
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Brain MRI Segmentation Algorithm Based on K-means and Graph Cut
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    为了克服原始图割算法在用户选定的像素种子点较少情况下,目标边界容易出现错分这一现象,本文提出了基于K-means和图割(Graph cut,GC)算法相结合的交互式K-均值图割(K-means and graph cut,KMGC)算法,对脑部核磁共振图像(Magnetic resonance image,MRI) 进行交互式操作,该算法通过K-means聚类,对脑部MRI的灰度不均匀性进行了处理,在此基础上,再使用图割算法进一步对脑部MRI进行细化,从而达到有效地分割脑白质和脑 灰质的目的。本文分别在仿真和真实的脑部MRI数据上进行了大量的实验,分别从定量分析和定性分析两个角度对实验结果进行了分析,并与其他分割算法进行了对比,对比实验结果标明,KMGC算法能够有效地对脑部MRI进行分割,并在分割效果上优于其他算法。

    Abstract:

    To overcome the target boundary prone to be misclassification for an original image when the user-selected seed pixels become less in the graph cut algorithm. An interactive K-means and graph cut algorithm (KMGC) is proposed in the combination of the K-means with graph cut(GC) algorithm and the interactive segmentation with brain magnetic resonance image (MRI). The MRI intensity inhomogeneity is processed by K-means clustering algorithm. On this basis, the graph cut algorithm will further refine the MRI, so as to obtain effective segmentation of white matter and gray matter. We implement extensive segmentation experiments using both synthetic and real brain MRIs. Quantitative and qualitative analyses are carried out about the experimental results, and the results are compared with other segmentation algorithms. The experimental results show that the KMGC algorithm can effectively divide the brain MRI, and outperform others on the segmentation effect.

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田换覃晓元昌安 刘致锦廖剑平.基于K-means和图割的脑部MRI分割算法[J].数据采集与处理,2016,31(5):974-982

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  • 在线发布日期: 2018-04-09