Yu Donggang, Pham Tuan D, Zhou Xiaobo
School of Design, Communication and Information Technology, The University of Newcastle, Callaghan, NSW 2308, Australia.
Comput Biol Med. 2009 Jan;39(1):27-39. doi: 10.1016/j.compbiomed.2008.10.006. Epub 2008 Dec 12.
Automated analysis and recognition of cell-nuclear phases using fluorescence microscopy images play an important role for high-content screening. A major task of automated imaging based high-content screening is to segment and reconstruct each cell from the touching cell images. In this paper we present new useful method for recognizing morphological structural models of touching cells, detecting segmentation points, determining the number of segmented cells in touching cell image, finding the related data of segmented cell arcs and reconstructing segmented cells. The conceptual frameworks are based on the morphological structures where a series of structural points and their morphological relationships are established. Experiment results have shown the efficient application of the new method for analysis and recognition of touching cell images of high-content screening.
利用荧光显微镜图像对细胞核阶段进行自动分析和识别，在高内涵筛选中发挥着重要作用。基于自动成像的高内涵筛选的一项主要任务是从相互接触的细胞图像中分割并重建每个细胞。在本文中，我们提出了一种新的实用方法，用于识别相互接触细胞的形态结构模型、检测分割点、确定相互接触细胞图像中分割细胞的数量、找到分割细胞弧的相关数据以及重建分割细胞。概念框架基于形态结构，在其中建立了一系列结构点及其形态关系。实验结果表明，该新方法在高内涵筛选的相互接触细胞图像分析和识别中得到了有效应用。
