DetecTiff©: A Novel Image Analysis Routine for High-Content Screening Microscopy
DetecTiff©: A Novel Image Analysis Routine for High-Content Screening Microscopy
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DOI：
10.1177/1087057109339523
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发表时间：
2009-09-01
影响因子：
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通讯作者：
Runz, Heiko
中科院分区：
文献类型：
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作者：
Gilbert, Daniel F.;Meinhof, Till;Runz, Heiko
在这篇文章中，作者描述了图像分析软件DetecTiff（c），它允许从数字图像中完全自动识别和量化对象。基于LabView（c）的例程的核心模块是一种结构识别算法，该算法以迭代方式从显微图像中采用强度阈值和大小相关的粒子滤波。检测到的结构被转换成模板，用于定量图像分析。DetecTiff（c）可处理多个检测通道，并提供模板组织和快速解释采集数据的功能。作者证明了DetecTiff（c）用于自动分析荧光标记的低密度脂蛋白的细胞摄取以及来自各种生物医学应用的各种其他图像数据集的适用性。此外，还将DetecTiff（c）的性能与现有的图像分析工具进行了比较。结果表明，DetecTiff可以高一致性地应用于图像数据的自动定量分析（例如，来自大规模功能性PNAi筛选项目）。（Journal of Biomolecular Screening 2009：944-955）
In this article, the authors describe the image analysis software DetecTiff (c), which allows fully automated object recognition and quantification from digital images. The core module of the LabView (c)-based routine is an algorithm for structure recognition that employs intensity thresholding and size-dependent particle filtering from microscopic images in an iterative manner. Detected structures are converted into templates, which are used for quantitative image analysis. DetecTiff (c) enables processing of multiple detection channels and provides functions for template organization and fast interpretation of acquired data. The authors demonstrate the applicability of DetecTiff (c) for automated analysis of cellular uptake of fluorescence-labeled low-density lipoproteins as well as diverse other image data sets from a variety of biomedical applications. Moreover, the performance of DetecTiff (c) is compared with preexisting image analysis tools. The results show that DetecTiff, can be applied with high consistency for automated quantitative analysis of image data (e.g., from large-scale functional PNAi screening projects). (Journal of Biomolecular Screening 2009:944-955)