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

• Published in: Journal of structural biology Vol. 216; no. 1; p. 108044
• Main Authors: Hou, Gaoxin, Yang, Zhidong, Zang, Dawei, Fernández, Jose-Jesus, Zhang, Fa, Han, Renmin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2024
• Subjects: Algorithms; Electron microscopy; Electrons; Fiducial marker detection; Fiducial Markers; Microscopy; Template matching; Wavelet transform; Wavelet transform; Fiducial marker detection; Electron microscopy; Template matching
• ISSN: 1047-8477; 1095-8657
• DOI: 10.1016/j.jsb.2023.108044

[Display omitted] •A novel template generation algorithm is proposed. By combining wavelet transform and a criterion of shape, a high-quality template will be derived.•A novel statistic-based filter strategy is proposed to improve the quality of the candidates by removing the candidates that have a low likelihood of being a fiducial marker.•A refined strategy is proposed to determine the accurate locations of fiducial markers. 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.