Literature DB >> 21972793

Segmentation of virus particle candidates in transmission electron microscopy images.

G Kylberg1, M Uppström, K-O Hedlund, G Borgefors, I-M Sintorn.   

Abstract

In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.
© 2011 The Authors Journal of Microscopy © 2011 Royal Microscopical Society.

Mesh:

Year:  2011        PMID: 21972793     DOI: 10.1111/j.1365-2818.2011.03556.x

Source DB:  PubMed          Journal:  J Microsc        ISSN: 0022-2720            Impact factor:   1.758


  3 in total

1.  Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

Authors:  Eisuke Ito; Takaaki Sato; Daisuke Sano; Etsuko Utagawa; Tsuyoshi Kato
Journal:  Food Environ Virol       Date:  2018-01-19       Impact factor: 2.778

2.  Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer.

Authors:  Jinghua Zhang; Chen Li; Yimin Yin; Jiawei Zhang; Marcin Grzegorzek
Journal:  Artif Intell Rev       Date:  2022-05-04       Impact factor: 9.588

3.  Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification.

Authors:  Loris Nanni; Sheryl Brahnam; Stefano Ghidoni; Alessandra Lumini
Journal:  Comput Intell Neurosci       Date:  2015-08-27
  3 in total

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