title: A novel fully automatic scheme for fiducial marker-based alignment in electron tomography.
abstract: Although the topic of fiducial marker-based alignment in electron tomography (ET) has been widely discussed for decades, alignment without human intervention remains a difficult problem. Specifically, the emergence of subtomogram averaging has increased the demand for batch processing during tomographic reconstruction; fully automatic fiducial marker-based alignment is the main technique in this process. However, the lack of an accurate method for detecting and tracking fiducial markers precludes fully automatic alignment. In this paper, we present a novel, fully automatic alignment scheme for ET. Our scheme has two main contributions: First, we present a series of algorithms to ensure a high recognition rate and precise localization during the detection of fiducial markers. Our proposed solution reduces fiducial marker detection to a sampling and classification problem and further introduces an algorithm to solve the parameter dependence of marker diameter and marker number. Second, we propose a novel algorithm to solve the tracking of fiducial markers by reducing the tracking problem to an incomplete point set registration problem. Because a global optimization of a point set registration occurs, the result of our tracking is independent of the initial image position in the tilt series, allowing for the robust tracking of fiducial markers without pre-alignment. The experimental results indicate that our method can achieve an accurate tracking, almost identical to the current best one in IMOD with half automatic scheme. Furthermore, our scheme is fully automatic, depends on fewer parameters (only requires a gross value of the marker diameter) and does not require any manual interaction, providing the possibility of automatic batch processing of electron tomographic reconstruction.
authors.affiliation: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing, China.
authors.forename: Renmin
authors.initials: R
authors.lastname: Han
authors.affiliation: Beijing Institute of Technology, Beijing, China.
authors.forename: Liansan
authors.initials: L
authors.lastname: Wang
authors.affiliation: State Key Lab for Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Liu
authors.affiliation: University of Chinese Academy of Sciences, Beijing, China; National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China; Center for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China. Electronic address: feisun@ibp.ac.cn.
authors.forename: Fei
authors.initials: F
authors.lastname: Sun
authors.affiliation: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: zhangfa@ict.ac.cn.
authors.forename: Fa
authors.initials: F
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001331:Automation; D002502:Centrioles; D055032:Electron Microscope Tomography / Q000379:methods*; D057918:Fiducial Markers; D006433:Hemocyanins / Q000032:analysis; D007091:Image Processing, Computer-Assisted / Q000379:methods*
chemical_list: D006433:Hemocyanins
keywords: Alignment; Electron tomography; Four point invariant; Point set registration