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MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template

文献类型：期刊论文

作者Hou, Gaoxin6; Yang, Zhidong4,5; Zang, Dawei5; Fernandez, Jose-Jesus3; Zhang, Fa2; Han, Renmin1,6
刊名JOURNAL OF STRUCTURAL BIOLOGY
出版日期2024-03-01
卷号216期号:1页码:13
关键词Electron microscopy Wavelet transform Template matching Fiducial marker detection
ISSN号1047-8477
DOI10.1016/j.jsb.2023.108044
英文摘要Fiducial marker detection in electron micrographs becomes an important and challenging task with the development of large-field electron microscopy. The fiducial marker detection plays an important role in several steps during the process of electron micrographs, such as the alignment and parameter calibrations. However, limited by the conditions of low signal-to-noise ratio (SNR) in the electron micrographs, the performance of fiducial marker detection is severely affected. In this work, we propose the MarkerDetector, a novel algorithm for detecting fiducial markers in electron micrographs. The proposed MarkerDetector is built upon the following contributions: Firstly, a wavelet-based template generation algorithm is devised in MarkerDetector. By adopting a shape-based criterion, a high-quality template can be obtained. Secondly, a robust marker determination strategy is devised by utilizing statistic-based filtering, which can guarantee the correctness of the detected fiducial markers. The average running time of our algorithm is 1.67 seconds with promising accuracy, indicating its practical feasibility for applications in electron micrographs.
资助项目Natural Science Foundation of Shandong Province[62072441] ; Natural Science Foundation of Shandong Province[32241027] ; King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA)[ZR2023YQ057] ; Youth Innovation Promotion Association CAS[URF/1/4352-01-01] ; Youth Innovation Promotion Association CAS[REI/1/5234-01-01] ; Youth Innovation Promotion Association CAS[REI/1/5414-01-01] ; Young Scientists Fund of the National Natural Science Foundation of China[61902373] ; Foundation of the Chinese Academy of Sciences, China ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB24050300] ; Strategic Priority Research Program of the Chinese Academy of Sciences, China[XDB44030300] ; Shanghai Municipal Science and Technology Major Project ; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI)[2018SHZDZX01] ; Instrument Improvement Funds of Shandong University Public Technology Platform
WOS研究方向Biochemistry & Molecular Biology ; Biophysics ; Cell Biology
语种英语
WOS记录号WOS:001140177000001
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
源URL[http://119.78.100.204/handle/2XEOYT63/38423]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Fa; Han, Renmin
作者单位1.King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr CBRC, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 23955, Saudi Arabia
2.Beijing Inst Technol, Sch Med Technol, Beijing 100081, Peoples R China
3.Spanish Natl Res Council CINN CSIC, Hlth Res Inst Asturias ISPA, Av Hosp Univ S-N, Oviedo 33011, Spain
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, Peoples R China
6.Shandong Univ, Res Ctr Math & Interdisciplinary Sci, Frontiers Sci Ctr Nonlinear Expectat, Minist Educ, Qingdao 266237, Peoples R China
推荐引用方式
GB/T 7714		 Hou, Gaoxin,Yang, Zhidong,Zang, Dawei,et al. MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template[J]. JOURNAL OF STRUCTURAL BIOLOGY,2024,216(1):13.
APA Hou, Gaoxin,Yang, Zhidong,Zang, Dawei,Fernandez, Jose-Jesus,Zhang, Fa,&Han, Renmin.(2024).MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template.JOURNAL OF STRUCTURAL BIOLOGY,216(1),13.
MLA Hou, Gaoxin,et al."MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template".JOURNAL OF STRUCTURAL BIOLOGY 216.1(2024):13.

入库方式： OAI收割

来源：计算技术研究所

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