High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion

被引：68

作者：

Quan, Tingwei ^{[1
,2
,3
]}

Zhu, Hongyu ^{[1
,2
]}

Liu, Xiaomao ^{[4
]}

Liu, Yongfeng ^{[5
]}

Ding, Jiuping ^{[5
]}

Zeng, Shaoqun ^{[1
,2
]}

Huang, Zhen-Li ^{[1
,2
]}

机构：

[1] Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Britton Chance Ctr Biomed Photon, Wuhan 430074, Peoples R China

[2] Huazhong Univ Sci & Technol, Minist Educ, Key Lab Biomed Photon, Wuhan 430074, Peoples R China

[3] Hubei Univ Educ, Sch Math & Econ, Wuhan 430205, Peoples R China

[4] Huazhong Univ Sci & Technol, Sch Math & Stat, Wuhan 430074, Peoples R China

[5] Huazhong Univ Sci & Technol, Coll Life Sci & Technol, Minist Educ, Key Lab Mol Biophys, Wuhan 430074, Peoples R China

来源：

OPTICS EXPRESS | 2011年 / 19卷 / 18期

基金：

中国国家自然科学基金;

关键词：

IMAGE-ANALYSIS; MICROSCOPY; CELLS;

D O I：

10.1364/OE.19.016963

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Localization-based super-resolution microscopy (or called localization microscopy) rely on repeated imaging and localization of active molecules, and the spatial resolution enhancement of localization microscopy is built upon the sacrifice of its temporal resolution. Developing algorithms for high-density localization of active molecules is a promising approach to increase the speed of localization microscopy. Here we present a new algorithm called SSM_BIC for such purpose. The SSM_BIC combines the advantages of the Structured Sparse Model (SSM) and the Bayesian Information Criterion (BIC). Through simulation and experimental studies, we evaluate systematically the performance between the SSM_BIC and the conventional Sparse algorithm in high-density localization of active molecules. We show that the SSM_BIC is superior in processing single molecule images with weak signal embedded in strong background. (C) 2011 Optical Society of America

引用

页码：16963 / 16974

页数：12

共 29 条

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