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An automated high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

机译：一种自动化的高通量方法用于标准化图像颜色配置文件以改善基于图像的植物表型

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摘要

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.
机译：高通量表型已经成为研究植物生物学的有力方法。生成基于大型图像的数据集并使用自动图像分析管道进行分析。与这些分析相关的主要挑战是图像质量的变化，这可能会无意间使结果产生偏差。图像由称为像素的元数据组成，这些数据元由R，G和B值组成，并以网格形式排列。许多因素（例如图像亮度）可能会影响所捕获图像的质量。这些因素会改变图像中像素的值，因此可能会使数据和下游分析产生偏差。在这里，我们提供了一种自动方法来调整基于图像的数据集，从而使亮度，对比度和颜色配置文件标准化。校正方法是线性模型的集合，该线性模型根据颜色的参考面板调整像素元组。我们将此技术应用于在高通量成像设备中拍摄的一组图像，并成功检测到图像数据集中的差异。在这种情况下，变化是由于整个实验过程中与温度有关的光强度所致。使用这种校正方法，我们能够对整个数据集中的图像进行标准化，并且表明该校正增强了我们准确量化每幅图像中形态学测量值的能力。我们在本文提供的高通量管道中实现了该技术，并且还在PlantCV中实现了该技术。

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