title: A divide and conquer strategy for the maximum likelihood localization of low intensity objects.
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.
authors: Alexander
authors: A
authors: Krull
authors: André
authors: A
authors: Steinborn
authors: Vaishnavi
authors: V
authors: Ananthanarayanan
authors: Damien
authors: D
authors: Ramunno-Johnson
authors: Uwe
authors: U
authors: Petersohn
authors: Iva M
authors: IM
authors: Tolić-Nørrelykke
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012568:Schizosaccharomyces / Q000166:cytology* / Q000378:metabolism*; D029702:Schizosaccharomyces pombe Proteins / Q000378:metabolism*; D012680:Sensitivity and Specificity; D013347:Subcellular Fractions / Q000378:metabolism / Q000648:ultrastructure
chemical_list: D029702:Schizosaccharomyces pombe Proteins