Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification
Unsupervised Segmentation of Overlapped Nuclei Using Bayesian Classification
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DOI：
10.1109/tbme.2010.2060486
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发表时间：
2010-12-01
影响因子：
4.6
通讯作者：
Oh, Sukjoong
中科院分区：
文献类型：
--
作者：
Jung, Chanho;Kim, Changick;Oh, Sukjoong
在全自动细胞提取过程中，要克服的主要问题之一是与提取重叠的细胞核相关的问题，因为这样的细胞核通常会影响细胞图像的定量分析。在本文中，我们提出了一个无监督贝叶斯分类方案分离重叠核。所提出的方法首先涉及将距离变换应用于重叠的核。该算法将距离变换生成的地形表面视为高斯混合体。为了学习的地形表面的分布，参数的期望最大化（EM）算法。执行聚类验证以确定有多少个核重叠。我们的分割方法结合了先验知识的规则形状的细胞核，以产生更准确的分割结果。实验结果表明，该方法产生上级分割性能，比传统的计划。
In a fully automatic cell extraction process, one of the main issues to overcome is the problem related to extracting overlapped nuclei since such nuclei will often affect the quantitative analysis of cell images. In this paper, we present an unsupervised Bayesian classification scheme for separating overlapped nuclei. The proposed approach first involves applying the distance transform to overlapped nuclei. The topographic surface generated by distance transform is viewed as a mixture of Gaussians in the proposed algorithm. In order to learn the distribution of the topographic surface, the parametric expectation-maximization (EM) algorithm is employed. Cluster validation is performed to determine how many nuclei are overlapped. Our segmentation approach incorporates a priori knowledge about the regular shape of clumped nuclei to yield more accurate segmentation results. Experimental results show that the proposed method yields superior segmentation performance, compared to those produced by conventional schemes.