Protein & Cell >

2015 , Vol. 6 >Issue 3: 211 - 220

DOI: https://doi.org/10.1007/s13238-015-0133-9

RESEARCH ARTICLE

Bayesian localization microscopy based on intensity distribution of fluorophores

  • Fan Xu 1,2 ,
  • Mingshu Zhang 3 ,
  • Zhiyong Liu 1 ,
  • Pingyong Xu , 3 ,
  • Fa Zhang , 1
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  • 1. Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. Laboratory of Non Coding RNA, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China

Received date: 03 Dec 2014

Accepted date: 31 Dec 2014

Published date: 01 Apr 2015

Copyright

2014 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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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.

Key words： super-resolution; fluorescence image; 3B; intensity distribution

Cite this article

Fan Xu , Mingshu Zhang , Zhiyong Liu , Pingyong Xu , Fa Zhang . Bayesian localization microscopy based on intensity distribution of fluorophores[J]. Protein & Cell, 2015 , 6(3) : 211 -220 . DOI: 10.1007/s13238-015-0133-9

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