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Updated: May 14, 2026

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

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Published on: April 9, 2017

Automatic virus particle selection--the entropy approach.

Maria da Conceição M Sangreman Proenca¹, J F Moura Nunes, A P Alves de Matos

¹Faculty of Sciences, University of Lisbon, Lisboa, Portugal. mcproenca@fc.ul.pt

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2013

Summary

This summary is machine-generated.

This study presents an automated method for identifying icosahedral virus particles in transmission electron microscopy (TEM) images. The approach accurately detects and validates viral particles using image analysis and feature extraction techniques.

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In Vitro Selection of Aptamers to Differentiate Infectious from Non-Infectious Viruses

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Area of Science:

Structural biology
Biophysics
Computational biology

Background:

Accurate identification of icosahedral virus particles in transmission electron microscopy (TEM) images is crucial for structural and functional analysis.
Manual identification can be time-consuming and prone to subjective errors.

Purpose of the Study:

To develop and validate a fully automatic approach for locating icosahedral virus particles in TEM images.
To improve the efficiency and objectivity of viral particle detection in microscopy data.

Main Methods:

Automatic segmentation of entropy-proportion images within defined regions of interest.
Utilizing morphological features for initial candidate selection with a low threshold to minimize false negatives.
Employing a credibility test based on radial intensity profiles and texture analysis for candidate validation.
Final outlier removal using a discrimination plan in a three-parameter space.

Main Results:

The automated method successfully identifies icosahedral virus particles.
The approach incorporates multiple validation steps to ensure high accuracy and reduce false positives/negatives.
The method is designed to be robust across different input images.

Conclusions:

The developed automated approach offers an efficient and reliable tool for detecting icosahedral virus particles in TEM images.
This method has the potential to significantly aid researchers in structural virology and related fields.
The fully automatic nature of the technique streamlines the analysis of large microscopy datasets.