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An Automated Image Analysis System to Measure and Count Organisms in Laboratory Microcosms

机译:自动化图像分析系统用于测量和计数实验室微观世界中的生物

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摘要

1. Because of recent technological improvements in the way computer and digital camera perform, the potential use of imaging for contributing to the study of communities, populations or individuals in laboratory microcosms has risen enormously. However its limited use is due to difficulties in the automation of image analysis. 2. We present an accurate and flexible method of image analysis for detecting, counting and measuring moving particles on a fixed but heterogeneous substrate. This method has been specifically designed to follow individuals, or entire populations, in experimental laboratory microcosms. It can be used in other applications. 3. The method consists in comparing multiple pictures of the same experimental microcosm in order to generate an image of the fixed background. This background is then used to extract, measure and count the moving organisms, leaving out the fixed background and the motionless or dead individuals. 4. We provide different examples (springtails, ants, nematodes, daphnia) to show that this non intrusive method is efficient at detecting organisms under a wide variety of conditions even on faintly contrasted and heterogeneous substrates. 5. The repeatability and reliability of this method has been assessed using experimental populations of the Collembola Folsomia candida. 6. We present an ImageJ plugin to automate the analysis of digital pictures of laboratory microcosms. The plugin automates the successive steps of the analysis and recursively analyses multiple sets of images, rapidly producing measurements from a large number of replicated microcosms.
机译:1.由于计算机和数码相机的工作方式最近有了技术上的改进,成像技术在实验室微观世界中对社区,人群或个人的研究做出了巨大的贡献。然而,由于图像分析自动化的困难,其使用受到限制。 2.我们提出了一种准确,灵活的图像分析方法,用于检测,计数和测量固定但异质的基材上的运动颗粒。该方法经过专门设计,可以跟踪实验实验室缩影中的个人或整个人群。可以在其他应用程序中使用。 3.该方法包括比较同一实验缩影的多张图片,以生成固定背景的图像。然后,使用该背景来提取,测量和计数运动的生物,而忽略固定的背景以及静止不动或死亡的个体。 4.我们提供了不同的示例(跳尾,蚂蚁,线虫,水蚤),以表明这种非侵入性方法即使在微弱对比和异质的底物上也能有效检测各种条件下的生物。 5.已经使用Collembola Folsomia candida的实验种群评估了该方法的可重复性和可靠性。 6.我们提供了一个ImageJ插件来自动分析实验室微观世界的数字图片。该插件可自动执行连续的分析步骤并递归分析多组图像,从而从大量复制的微观世界中快速生成测量结果。

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