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Using Time Courses To Enrich the Information Obtained from Images of Crystallization Trials

机译：使用时间课程来丰富从结晶试验图像中获得的信息

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Visual identification of crystals from experimental setups (droplets) underpins the field of structural biology, and there is an increasing use of automation to capture records of crystallization trials -snapshots of the experiments over time-which are then used to identify crystals for subsequent analysis. Here we describe an algorithm that locates differences between images within an image time-course of a crystallization experiment. A user-accessible “front end” to this functionality is provided by tagging images, which have been identified as changed, and using an existing (commercial) viewing software package to display the tagged images. The existence of change appears to be a powerful tool to filter out images that do not need further human examination, as the rate of false negatives is low and the method is general. We identify significant change in a time sequence of crystallization images by using a process of image alignment, drop recognition, masking, filtering, and comparison. We propose that this process might be a powerful way of picking up changes in crystallization experiments which happen after weeks or months.
机译：从实验装置（液滴）中目视鉴定晶体是结构生物学领域的基础，并且越来越多地使用自动化来捕获结晶试验的记录-随时间推移的实验快照-然后用于鉴定晶体以进行后续分析。在这里，我们描述了一种在结晶实验的图像时间过程中定位图像之间差异的算法。通过标记已被标识为已更改的图像并使用现有的（商业）查看软件包来显示标记的图像，可以为用户提供此功能的“前端”。更改的存在似乎是过滤掉不需要进一步人工检查的图像的强大工具，因为假阴性的比率很低并且该方法很通用。通过使用图像对齐，墨滴识别，掩膜，过滤和比较过程，我们可以确定结晶图像时间序列中的重大变化。我们建议，此过程可能是了解数周或数月后发生的结晶实验变化的有效方法。

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