Krull Alexander, Steinborn André, Ananthanarayanan Vaishnavi, Ramunno-Johnson Damien, Petersohn Uwe, Tolić-Nørrelykke Iva M
Opt Express. 2014 Jan 13;22(1):210-28. doi: 10.1364/OE.22.000210.
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.
在细胞生物学和其他领域中，图像中亚分辨率物体的自动精确定位是一项重要工具。信号常常会被多种形式的噪声所干扰，包括电子倍增电荷耦合器件（EMCCD）放大产生的过量噪声。在此，我们提出了新颖的嵌套最大似然算法（NMLA），该算法通过在无过量噪声系统中反复求解单个发射器的独立定位任务，解决了在受过量噪声影响的情况下对多个重叠发射器进行定位的问题。与通用优化技术相比，NMLA显著提高了可扩展性和鲁棒性。我们的方法已成功应用于荧光蛋白的体内定位。
