Department of Physiology, School of Medical Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand.
Microsc Microanal. 2010 Feb;16(1):64-72. doi: 10.1017/S143192760999122X.
Localization microscopy techniques based on localizing single fluorophore molecules now routinely achieve accuracies better than 30 nm. Unlike conventional optical microscopies, localization microscopy experiments do not generate an image but a list of discrete coordinates of estimated fluorophore positions. Data display and analysis therefore generally require visualization methods that translate the position data into conventional images. Here we investigate the properties of several widely used visualization techniques and show that a commonly used algorithm based on rendering Gaussians may lead to a 1.44-fold loss of resolution. Existing methods typically do not explicitly take sampling considerations into account and thus may produce spurious structures. We present two additional visualization algorithms, an adaptive histogram method based on quad-trees and a Delaunay triangulation based visualization of point data that address some of these deficiencies. The new visualization methods are designed to suppress erroneous detail in poorly sampled image areas but avoid loss of resolution in well-sampled regions. A number of criteria for scoring visualization methods are developed as a guide for choosing among visualization methods and are used to qualitatively compare various algorithms.
基于定位单荧光分子的定位显微镜技术现在通常可以达到优于 30nm 的精度。与传统的光学显微镜不同，定位显微镜实验不会生成图像，而是生成估计荧光位置的离散坐标列表。因此，数据显示和分析通常需要可视化方法将位置数据转换为常规图像。在这里，我们研究了几种广泛使用的可视化技术的特性，并表明一种常用的基于渲染高斯函数的算法可能导致分辨率损失 1.44 倍。现有的方法通常没有明确考虑采样考虑因素，因此可能会产生虚假结构。我们提出了两种额外的可视化算法，一种基于四叉树的自适应直方图方法和一种基于 Delaunay 三角剖分的点数据可视化，这些算法解决了其中的一些缺陷。新的可视化方法旨在抑制采样不良的图像区域中的错误细节，但避免在采样良好的区域中损失分辨率。还开发了一些用于对可视化方法进行评分的标准，作为在可视化方法之间进行选择的指南，并用于定性比较各种算法。
