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Texture Indicators for Segmentation of Polyomavirus Particles in Transmission Electron Microscopy Images

Published online by Cambridge University Press:  18 June 2013

Maria C. Proença ,
José F.M. Nunes  and
António P.A. de Matos
Maria C. Proença*
Affiliation:
Laboratory of Optics, Lasers and Systems, Physics Department, Faculty of Sciences of the University of Lisbon, Edifício C8, Campo Grande, 1749-016 Lisboa, Portugal Centro de Estudos do Ambiente e do Mar (CESAM/FCUL), Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
José F.M. Nunes
Affiliation:
Serviço de Anatomia Patológica, Instituto Português de Oncologia, Rua Prof. Lima Basto, 1099-023 Lisboa, Portugal
António P.A. de Matos
Affiliation:
Centro de Estudos do Ambiente e do Mar (CESAM/FCUL), Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal Centro Hospitalar de Lisboa Central, Hospital Curry Cabral, Rua da Beneficência 8, 1069-166 Lisboa, Portugal Centro de Investigação Interdisciplinar Egas Moniz (CiiEM), Quinta da Granja, Monte de Caparica, 2829-511 Caparica, Portugal
*
*Corresponding author. E-mail: mcproenca@fc.ul.pt
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Abstract

A fully automatic approach to locate polyomavirus particles in transmission electron microscopy images is presented that can localize intact particles, many damaged capsids, and an acceptable percentage of superposed ones. Performance of the approach is quantified in 25 electron micrographs containing nearly 390 particles and compared with the interpretation of the micrographs by two independent electron microscopy experts. All parameterization is based on the particle expected dimensions. This approach uses indicators calculated from the local co-occurrence matrix of gray levels to assess the textured pattern typical of polyomavirus and prune the initial set of candidates. In more complicated backgrounds, about 2–10% of the elements survive. A restricted set of the accepted points is used to evaluate the typical average and variance and to reduce the set of survivors accordingly. These intermediate points are evaluated using (i) a statistical index concerning the radiometric distribution of a circular neighborhood around the centroid of each candidate and (ii) a structural index resuming the expected morphological characteristics of eight radial intensity profiles encompassing the area of the possible particle. This hierarchical approach attains 90% efficiency in the detection of entire virus particles, tolerating a certain lack of differentiation in the borders and a certain amount of shape alterations.

Keywords

polyomavirusautomatic particle pickingtexturetransmission electron microscopy imagespattern classificationsegmentation
Type
Portuguese Society for Microscopy
Information
Microscopy and Microanalysis , Volume 19 , Issue 5 , October 2013 , pp. 1170 - 1182
Copyright
Copyright © Microscopy Society of America 2013 

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