title: Structural anisotropy quantification improves the final superresolution image of localization microscopy.
abstract: 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.
authors.affiliation: 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.
authors.forename: Yina
authors.initials: Y
authors.lastname: Wang
authors.affiliation: 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.
authors.forename: Zhen-Li
authors.initials: ZL
authors.lastname: Huang
mesh_terms: D016880:Anisotropy; D003198:Computer Simulation; D005456:Fluorescent Dyes; D008856:Microscopy, Fluorescence; D008954:Models, Biological; D061848:Optical Imaging / Q000295:instrumentation / Q000379:methods*
chemical_list: D005456:Fluorescent Dyes