A novel cell segmentation method and cell phase identification using Markov model.
A novel cell segmentation method and cell phase identification using Markov model.
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DOI：
10.1109/titb.2008.2007098
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发表时间：
2009-03
期刊：
影响因子：
--
通讯作者：
Wong ST
中科院分区：
文献类型：
--
作者：
Zhou X;Li F;Yan J;Wong ST
光学显微镜正在成为药物发现和生命科学研究的重要技术。用于分析光学显微镜图像的方法通常分为两类：自动和手动方法。然而，现有的自动化系统在处理大量的延时显微镜图像是相当有限的，因为细胞的行为和形态变化的复杂性。另一方面，手动方法非常耗时。在本文中，我们提出了一个有效的自动化，定量分析系统，可用于分割，跟踪，并有效地和高效地观察细胞核的大群体的细胞周期行为。我们使用自适应阈值和分水岭算法的细胞核分割，其次是一个片段合并的方法，结合两个评分模型的基础上的趋势和无趋势的功能。利用时间推移数据的上下文信息，通过马尔可夫模型准确地识别细胞核的相位。实验结果表明，该系统是有效的核分割和相位识别。
Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.