Correction to: Multi-template matching: a versatile tool for object-localization in microscopy images

AbstractWe implemented multiple template matching as both a Fiji plugin and a KNIME workflow, providing an easy-to-use method for the automatic localization of objects of interest in images. We demonstrate its application for the localization of entire or partial biological objects. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow can be downloaded from nodepit space or the associated GitHub repository. Python source codes and documentations are available on the following GitHub repositories: LauLauThom/MultiTemplateMatching and LauLauThom/MultipleTemplateMatching-KNIME.

Download Full-text

Multi-template matching: a versatile tool for object-localization in microscopy images

BMC Bioinformatics ◽

10.1186/s12859-020-3363-7 ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 2

Author(s):

Laurent S. V. Thomas ◽

Jochen Gehrig

Keyword(s):

Template Matching ◽

Object Localization ◽

Versatile Tool ◽

Microscopy Images

Download Full-text

Cascade template matching for object localization

2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) ◽

10.1109/cyber.2012.6320066 ◽

2012 ◽

Author(s):

Tianzhu Fang ◽

Yanyun Qu ◽

Xiaofeng Du

Keyword(s):

Template Matching ◽

Object Localization

Download Full-text

Knowledge-Based Object Localization in Scanning Electron Microscopy Images for Hardware Assurance

ISTFA 2020: Papers Accepted for the Planned 46th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2020p0020 ◽

2020 ◽

Author(s):

Hangwei Lu ◽

Ronald Wilson ◽

Nidish Vashistha ◽

Navid Asadizanjani ◽

Mark Tehranipoor ◽

...

Keyword(s):

Target Location ◽

Search Algorithm ◽

Object Localization ◽

Test Cases ◽

Sem Images ◽

Essential Step ◽

Knowledge Based ◽

Research Fields ◽

Trojan Detection ◽

Microscopy Images

Abstract Object localization is an essential step in image-based hardware assurance applications to navigate the view to the target location. Existing localization methods are well-developed for applications in many other research fields; however, limited study has been conducted to explore an accurate yet efficient solution in hardware assurance domain. To this end, this paper discusses the challenges of leveraging existing object localization methods from three aspects using the example scenario of IC Trojan detection and proposes a novel knowledge-based object localization method. The proposed method is inspired by the 2D string search algorithm; it also couples a mask window to preserve target topology, which enables multi-target localization. Evaluations are conducted on 61 test cases from five images of three node-technologies. The results validate the accuracy, time-efficiency, and the generalizability of the proposed method of locating multi-target from SEM images for hardware assurance applications.

Download Full-text

Automatic Detection of Spherical Particles from Spot-Scan Electron Microscopy Images

Microscopy and Microanalysis ◽

10.1017/s1431927695111915 ◽

1995 ◽

Vol 1 (5) ◽

pp. 191-201 ◽

Cited By ~ 1

Author(s):

Pamela A. Thuman-Commike ◽

Wah Chiu

Keyword(s):

Electron Microscopy ◽

Template Matching ◽

Spherical Particles ◽

Matching Algorithm ◽

Virus Particles ◽

Particle Selection ◽

Additional Image ◽

Particle Images ◽

Spatially Varying ◽

Microscopy Images

In this article we present a method to identify and extract spherical particle images from noisy spot-scan and flood beam electron microscopy images. We use a template matching algorithm with additional image preprocessing operations to allow consistent particle selection in spot-scan and other highly spatially varying images. In addition, this algorithm incorporates an automated image-cutting and edge-sewing mechanism that allows efficient particle selection despite the large size of electron microscopy images. We have tested this template matching algorithm on various spherical virus particle images with a large range of defocus values and have found that the particles are consistently selected in an accurately centered manner. In addition, this method is able to extract spherical virus particles from 400-kV electron microscopy images with defocus values of less than 1.0 μm underfocus where the particles are not readily visible to the human eye.

Download Full-text

Locating Macromolecular Assemblies in Cells by 2D Template Matching with cisTEM

10.1101/2021.04.20.440648 ◽

2021 ◽

Author(s):

Bronwyn A. Lucas ◽

Benjamin A. Himes ◽

Liang Xue ◽

Timothy Grant ◽

Julia Mahamid ◽

...

Keyword(s):

Template Matching ◽

Molecular Mechanisms ◽

Computational Cost ◽

Complementary Information ◽

Macromolecular Assemblies ◽

Versatile Tool ◽

A Cell ◽

High Computational Cost ◽

A Current

AbstractOver the last decade, single-particle electron cryo-microscopy has become one of the main techniques contributing to the growing library of high-resolution structures of macromolecules and their assemblies. For a full understanding of molecular mechanisms, however, it is important to place them into the broader context of a cell. Traditionally, this context can be visualized in 3D by electron cryo-tomography, and more recently, has also been studied by template matching of 2D images of cells and viruses. A current limitation of the latter approach is the high computational cost that limits the throughput and widespread adoption of this method. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software cisTEM that allows for easy scaling and improves the accessibility of this approach. We apply 2D template matching to identify ribosomes in images of frozen-hydrated Mycoplasma pneumoniae cells and demonstrate that it can function as a versatile tool for in situ visual proteomics and in situ structure determination. We compare the results with 3D template matching of tomograms acquired on identical sample locations. We identify strengths and weaknesses of both techniques which offer complementary information about target localization and identity.

Download Full-text

Locating Macromolecular Assemblies in Cells by 2D Template Matching with cisTEM

eLife ◽

10.7554/elife.68946 ◽

2021 ◽

Vol 10 ◽

Author(s):

Bronwyn A Lucas ◽

Benjamin A Himes ◽

Liang Xue ◽

Tim Grant ◽

Julia Mahamid ◽

...

Keyword(s):

High Resolution ◽

Template Matching ◽

Molecular Mechanisms ◽

Computational Cost ◽

Molecular Complexes ◽

Three Dimensions ◽

Macromolecular Assemblies ◽

False Positive Detection ◽

Versatile Tool

For a more complete understanding of molecular mechanisms, it is important to study macromolecules and their assemblies in the broader context of the cell. This context can be visualized at nanometer resolution in three dimensions (3D) using electron cryo-tomography, which requires tilt series to be recorded and computationally aligned, currently limiting throughput. Additionally, the high-resolution signal preserved in the raw tomograms is currently limited by a number of technical difficulties, leading to an increased false-positive detection rate when using 3D template matching to find molecular complexes in tomograms. We have recently described a 2D template matching approach that addresses these issues by including high-resolution signal preserved in single-tilt images. A current limitation of this approach is the high computational cost that limits throughput. We describe here a GPU-accelerated implementation of 2D template matching in the image processing software cisTEM that allows for easy scaling and improves the accessibility of this approach. We apply 2D template matching to identify ribosomes in images of frozen-hydrated Mycoplasma pneumoniae cells with high precision and sensitivity, demonstrating that this is a versatile tool for in situ visual proteomics and in situ structure determination. We benchmark the results with 3D template matching of tomograms acquired on identical sample locations and identify strengths and weaknesses of both techniques, which offer complementary information about target localization and identity.

Download Full-text