Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
Nat Commun. 2019 Feb 15;10(1):793. doi: 10.1038/s41467-019-08689-x.
The resolution of an imaging system is a key property that, despite many advances in optical imaging methods, remains difficult to define and apply. Rayleigh's and Abbe's resolution criteria were developed for observations with the human eye. However, modern imaging data is typically acquired on highly sensitive cameras and often requires complex image processing algorithms to analyze. Currently, no approaches are available for evaluating the resolving capability of such image processing algorithms that are now central to the analysis of imaging data, particularly location-based imaging data. Using methods of spatial statistics, we develop a novel algorithmic resolution limit to evaluate the resolving capabilities of location-based image processing algorithms. We show how insufficient algorithmic resolution can impact the outcome of location-based image analysis and present an approach to account for algorithmic resolution in the analysis of spatial location patterns.
成像系统的分辨率是一个关键特性，尽管在光学成像方法方面取得了许多进展，但仍然难以定义和应用。Rayleigh 和 Abbe 的分辨率准则是为人类观察而开发的。然而，现代成像数据通常是在高灵敏度相机上获取的，并且通常需要复杂的图像处理算法来进行分析。目前，尚无方法可用于评估这些图像处理算法的分辨能力，而这些算法现在是成像数据分析的核心，特别是基于位置的成像数据。我们使用空间统计学方法，开发了一种新的算法分辨率极限来评估基于位置的图像处理算法的分辨能力。我们展示了算法分辨率不足如何影响基于位置的图像分析的结果，并提出了一种在分析空间位置模式时考虑算法分辨率的方法。
