A divide and conquer strategy for the maximum likelihood localization of low intensity objects (CROSBI ID 209356)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Krull, Alexander ; Steinborn, André ; Ananthanarayanan, Vaishnavi ; Ramunno-Johnson, Damien ; Petersohn, Uwe ; Tolić-Norrelykke, Iva Marija
engleski
A divide and conquer strategy for the maximum likelihood localization of low intensity objects
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.
single-molecule microscopy ; low-density-lipoprotein ; EM algorithm ; tracking ; cell ; accuracy ; minimum ; motion
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