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Journal Article

A divide and conquer strategy for the maximum likelihood localization of low intensity objects.

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Krull,  Alexander
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

Steinborn,  André
Max Planck Society;

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Ananthanarayanan,  Vaishnavi
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Ramunno-Johnson,  Damien
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

Petersohn,  Uwe
Max Planck Society;

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Tolic-Norrelykke,  Iva M.
Max Planck Institute of Molecular Cell Biology and Genetics, Max Planck Society;

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Citation

Krull, A., Steinborn, A., Ananthanarayanan, V., Ramunno-Johnson, D., Petersohn, U., & Tolic-Norrelykke, I. M. (2014). A divide and conquer strategy for the maximum likelihood localization of low intensity objects. Optics Express, 22(1), 210-228.


Cite as: https://hdl.handle.net/21.11116/0000-0001-051F-8
Abstract
In cell biology and other fields the automatic accurate localization of sub-resolution objects in images is an important tool. The signal is often corrupted by multiple forms of noise, including excess noise resulting from the amplification by an electron multiplying charge-coupled device (EMCCD). Here we present our novel Nested Maximum Likelihood Algorithm (NMLA), which solves the problem of localizing multiple overlapping emitters in a setting affected by excess noise, by repeatedly solving the task of independent localization for single emitters in an excess noise-free system. NMLA dramatically improves scalability and robustness, when compared to a general purpose optimization technique. Our method was successfully applied for in vivo localization of fluorescent proteins.