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Nat Commun. 2019 Feb 15;10(1):793. doi: 10.1038/s41467-019-08689-x.

Resolution limit of image analysis algorithms.

Author information

1
Department of Mathematics, Imperial College London, London, SW7 2AZ, UK. e.cohen@imperial.ac.uk.
2
Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
3
Department of Molecular & Cellular Medicine, Texas A&M University, College Station, TX, 77843, USA.
4
Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA. raimund.ober@tamu.edu.
5
Department of Molecular & Cellular Medicine, Texas A&M University, College Station, TX, 77843, USA. raimund.ober@tamu.edu.
6
Centre for Cancer Immunology, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, UK. raimund.ober@tamu.edu.

Abstract

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.

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