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Resolution limit of image analysis algorithms

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If you have a question about this talk, please contact Hector Basevi.

Host: Dr Iain Styles

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 and had a major influence on the development of optical instruments. However, modern imaging data is typically acquired on highly sensitive cameras and often requires complex image processing algorithms to analyse. Up until this point, no approaches have been available for evaluating the resolving capability of such image processing algorithms that are now central to the analysis of imaging data. This is particularly relevant for the many modern imaging experiments and corresponding image processing algorithms for which the detection of objects (e.g., molecules, molecular complexes, subcellular organelles) form an integral aspect. Examples are localization-based superresolution experiments (PALM, STORM , etc), experiments to investigate the arrangement of molecular complexes on the cellular membrane such as clathrin-coated pits experiments tracking single particles or subcellular organelles etc. 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.

This is joint work with Prof Raimund Ober, Department of Biomedical Engineering, Texas A&M.


This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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