Resolution limit of image analysis algorithms

Edward A. K. Cohen (), Anish V. Abraham, Sreevidhya Ramakrishnan and Raimund J. Ober ()
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Edward A. K. Cohen: Imperial College London
Anish V. Abraham: Texas A&M University
Sreevidhya Ramakrishnan: Texas A&M University
Raimund J. Ober: Texas A&M University

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-08689-x

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