Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
IEEE Trans Biomed Eng. 2010 Dec;57(12):2825-32. doi: 10.1109/TBME.2010.2060486. Epub 2010 Jul 23.
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.
在全自动细胞提取过程中，需要克服的主要问题之一是提取重叠细胞核的问题，因为这些细胞核通常会影响细胞图像的定量分析。在本文中，我们提出了一种用于分离重叠细胞核的无监督贝叶斯分类方案。所提出的方法首先涉及到将重叠的细胞核应用于距离变换。所提出的算法中，距离变换生成的地形表面被视为高斯混合体。为了学习地形表面的分布，使用参数期望最大化（EM）算法。通过聚类验证来确定有多少个细胞核重叠。我们的分割方法结合了关于聚集细胞核规则形状的先验知识，以获得更准确的分割结果。实验结果表明，与传统方法相比，所提出的方法具有更好的分割性能。
