Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, 1037 Luoyu Road, Wuhan 430074, ChinabHuazhong University of Science and Technology, Key Laboratory of Biomedical P.
J Biomed Opt. 2016 Jul 1;21(7):76011. doi: 10.1117/1.JBO.21.7.076011.
Superresolution localization microscopy initially produces a dataset of fluorophore coordinates instead of a conventional digital image. Therefore, superresolution localization microscopy requires additional data analysis to present a final superresolution image. However, methods of employing the structural information within the localization dataset to improve the data analysis performance remain poorly developed. Here, we quantify the structural information in a localization dataset using structural anisotropy, and propose to use it as a figure of merit for localization event filtering. With simulated as well as experimental data of a biological specimen, we demonstrate that exploring structural anisotropy has allowed us to obtain superresolution images with a much cleaner background.
超分辨率定位显微镜最初会生成荧光团坐标数据集，而不是传统的数字图像。因此，超分辨率定位显微镜需要额外的数据分析才能呈现最终的超分辨率图像。然而，利用定位数据集的结构信息来提高数据分析性能的方法仍然发展得不够完善。在这里，我们使用结构各向异性来量化定位数据集的结构信息，并提出将其用作定位事件过滤的一种性能指标。通过模拟和生物样本的实验数据，我们证明了探索结构各向异性使我们能够获得背景更干净的超分辨率图像。
