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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: [email protected]
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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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References

Coudray, N., Hermann, G., Caujolle-Bert, D., Karathanou, A., Erne-Brand, F., Buessler, J.L., Daum, P., Plitzko, J.M., Chami, M., Mueller, U., Kihl, H., Urban, J.P., Engel, A. & Rémigy, H.W. (2011). Automated screening of 2D crystallization trials using transmission electron microscopy: A high-throughput tool-chain for sample preparation and microscopic analysis. J Struct Biol 173(2), 365–374.CrossRefGoogle ScholarPubMed
Goldsmith, C.S. & Miller, S.E. (2009). Modern uses of electron microscopy for detection of viruses. Clin Microbiol Rev 22(4), 552–563. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2772359 (accessed March 15, 2011).CrossRefGoogle ScholarPubMed
Haralick, R.M., Shanmugam, K. & Dinstein, I. (1973). Textural features for image classification. IEEE Trans SMC-3(6), 610–621.Google Scholar
Hermann, G., Coudray, N., Buessler, J.-L., Caujolle-Bert, D., Rémigy, H.-W. & Urban, J.-P. (2012). Animated-TEM: A toolbox for electron microscope automation based on image analysis. Mach Vis Appl 23, 691–711.CrossRefGoogle Scholar
Kylberg, G., Sintorn, I.-M., Uppström, M. & Ryner, M. (2009). Local intensity and PCA based detection of virus particle candidates in transmission electron microscopy images. Proceedings of the IEEE 6th International Symposium on Image and Signal Processing and Analysis, University of Salzburg, Salzburg, Austria, 426–431. CrossRefGoogle Scholar
Kylberg, G., Uppström, M., Hedlund, K.-O., Borgefors, G. & Sintorn, I.-M. (2012). Segmentation of virus particle candidates in transmission electron microscopy images. J Microsc 245(2), 140–147.CrossRefGoogle ScholarPubMed
Kylberg, G., Uppström, M. & Sintorn, I.-M. (2011). Virus texture analysis using local binary patterns and radial density profiles. Proceedings of Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications—16th Iberoamerican Congress on Pattern Recognition 7042, 573–580.Google Scholar
Lata, K.R., Penczek, P. & Frank, J. (1995). Automatic particle picking from electron micrographs. Ultramicroscopy 58, 381–391.CrossRefGoogle ScholarPubMed
Matuszewski, B.J. & Shark, L.-K. (2001). Hierarchical iterative Bayesian approach to automatic recognition of biological viruses in electron microscope images. Proceedings of IEEE 2001 International Conference on Image Processing, Thessaloniki, Greece, Vol. 2, pp. 347–350. CrossRefGoogle Scholar
Nicholson, W.V. & Glaeser, R.M. (2001). Review: Automatic particle detection in electron microscopy. J Struct Biol 133, 90–101. http://www.doc.ic.ac.uk/~pb401/VisInvis/archive/resources/Particle_Picking_Glaeser.pdf (accessed March 17, 2011).CrossRefGoogle ScholarPubMed
Plaisier, J.R., Koning, R.I., Koerten, H.K., van Heel, M. & Abrahams, J.P. (2004). TYSON: Robust searching, sorting and selecting of single particles in electron micrographs. J Struct Biol 145, 76–83.CrossRefGoogle ScholarPubMed
Proença, M.C., Moura Nunes, J.F. & Alves de Matos, A.P. (2013). Automatic virus particle selection—The entropy approach. IEEE-TIP 22(5), 1996–2003.Google ScholarPubMed
Roseman, A.M. (2004). FindEM—A fast, efficient program for automatic selection of particles from electron micrographs. J Struct Biol 145, 91–99.CrossRefGoogle ScholarPubMed
Ryner, M., Stromberg, J.-O., Soderberg-Nauclér, C. & Homman-Loudiyi, M. (2006). Identification and classification of human cytomegalovirus capsids in textured electron micrographs using deformed template matching. Virol J 3(57), http://www.virologyj.com/content/3/1/57 (accessed November 8, 2011).CrossRefGoogle ScholarPubMed
Schramlová, J., Arientová, S. & Hulínská, D. (2010). The role of electron microscopy in the rapid diagnosis. Folia Microbiol 55 (1), 88–101.CrossRefGoogle ScholarPubMed
Shah, A.K. & Stewart, P.L. (1998). QVIEW: Software for rapid selection of particles from digital electron micrographs. J Struct Biol 123, 17–21.CrossRefGoogle ScholarPubMed
Short, J.M. (2004). SLEUTH—A fast computer program for automatically detecting particles in electron microscope images. J Struct Biol 145, 100–110.CrossRefGoogle ScholarPubMed
Sintorn, I.-M., Homman-Loudiyi, M., Soderberg-Nauclér, C. & Borgefors, G. (2004). A refined circular template matching method for classification of human cytomegalovirus capsids in TEM images. Comput Methods and Programs Biomed 76, 95–102.CrossRefGoogle ScholarPubMed
Suloway, C., Pulokas, J., Fellmann, D., Cheng, A., Guerra, F., Quispe, J., Stagg, S., Potter, C.S. & Carragher, B. (2005). Automated molecular microscopy: The new Leginon system. J Struct Biol 151, 41–60.CrossRefGoogle ScholarPubMed
Tang, G., Peng, L., Baldwin, P.R., Mann, D.S., Jiang, W., Rees, I. & Ludtke, S.J. (2007). EMAN2: An extensible image processing suite for electron microscopy. J Struct Biol 157, 38–46.CrossRefGoogle ScholarPubMed
Zhu, Y., Carragher, B., Glaeser, R.M., Fellmann, D., Bajaj, C., Bern, M., Mouche, F., de Haas, F., Hall, R.J., Kriegman, D.J., Ludtke, S.J., Mallick, S.P., Penczek, P.A., Roseman, A.M., Sigworth, F.J., Volkmann, N. & Potter, C.S. (2004). Automatic particle selection: Results of a comparative study. J Struct Biol 145, 3–14. http://vision.ucsd.edu/kriegman-grp/papers/jsb04a.pdf.CrossRefGoogle ScholarPubMed