Bayesian localization microscopy based on intensity distribution of fluorophores.
10.1007/s13238-015-0133-9
- Author:
Fan XU
1
;
Mingshu ZHANG
;
Zhiyong LIU
;
Pingyong XU
;
Fa ZHANG
Author Information
1. Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
- Publication Type:Journal Article
- MeSH:
Animals;
Bayes Theorem;
COS Cells;
Cercopithecus aethiops;
Computer Simulation;
Green Fluorescent Proteins;
metabolism;
Microscopy, Fluorescence;
methods;
Molecular Imaging;
methods
- From:
Protein & Cell
2015;6(3):211-220
- CountryChina
- Language:English
-
Abstract:
Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data (3B) method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fluorophores in the cell to fit the data. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensity at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution (FID3B). We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the performance of our method using both simulated data and experimental data from cellular structures. The results confirm the effectiveness of our method.
