Abstract
TEM image processing tools are devised for the assessment of 2D-crystallization experiments. The algorithms search for the presence and assess the quality of crystalline membranes. The retained scenario emulates the decisions of a microscopist in selecting targets and assessing the sample. Crystallinity is automatically assessed through the diffraction patterns of high magnification images acquired on pertinent regions selected at lower magnifications. Further algorithms have been developed for membrane characterization. Tests on images of different samples, acquired on different microscopes led to good results.
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References
Wilson J (2004) Automated evaluation of crystallisation experiments. Crystallography Reviews 10(1):73–84.
Zhu X, Sun S, Cheng SE, Bern M (2004) Classification of protein crystallization imagery. EMBS-04, vol 3, pp 1628–1631.
Stahlberg H, Fotiadis D, Scheuring S, Rémigy H, Braun T, Mitsuoka K, Fujiyoshi Y, Engel A (2001) Two-dimensional crystals: a powerful approach to assess structure, function and dynamics of membrane proteins. FEBS Letters 504(3):166–172.
HT-3DEM. http://www.ht3dem.org.
Signorell GA, Kaufmann TC, Kukulski W, Engel A, Rémigy H (2007) Controlled 2D crystallization of membrane proteins using methyl-[beta]-cyclodextrin. Journal of Structural Biology 157(2):321–328.
Cheng A, Leung A, Fellmann D, Quispe J, Suloway C, Pulokas J, Carragher B, Potter CS (2007) Towards automated screening of two-dimensional crystals. Journal of Structural Biology 160(3):324–331.
Oostergetel GT, Keegstra W, Brisson A (1998) Automation of specimen selection and data acquisition for protein electron crystallography. Ultramicroscopy 74(1–2):47–59.
Coudray N, Buessler JL, Kihl H, Urban JP (2007) Automated image analysis for electron microscopy specimen assessment. EUSIPCO-07, Poznan, Poland, PTETiS Poznan, pp 120–124.
Coudray N, Buessler JL, Kihl H, Urban JP (2007) Multi-scale and first derivative analysis for edge detection in tem images. ICIAR-07, Montréal, Canada, Springer LNCS, vol 4633, pp 1005–1016.
Meyer F (1994) Topographic distance and watershed lines. Signal Processing 38(1):113–125.
Karathanou A, Buessler J-L, Kihl H, Urban J-P (2009) Background Extraction in Electron Microscope Images of Artificial Membranes. IFIP, Thessaloniki, Greece, Springer, vol 296, pp 165–173.
Hermann G, Karathanou A, Buessler JL, Urban JP (2009) Evaluation of membrane stacking in electron microscope images. In Digital Imaging Sensors and Applications, Part of the Imaging Science and Technology/SPIE, 21st Annual Symposium on Electronic Imaging, San Jose, CA, USA.
Henderson R, Baldwin JM, Downing KH, Lepault J, Zemlin F (1986) Structure of purple membrane from halobacterium halobium: recording, measurement and evaluation of electron micrographs at 3.5 Å resolution. Ultramicroscopy 19:147–178.
Acknowledgments
This work has been supported by the EU sixth framework (HT3DEM, LSHG-CT-2005-018811). We thank the Biozentrum of Basel and FEI company Eindhoven for the good collaboration and for providing the TEM images.
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Karathanou, A., Coudray, N., Hermann, G., Buessler, JL., Urban, JP. (2010). Automatic TEM Image Analysis of Membranes for 2D Crystal Detection. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_37
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_37
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