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Relevant bibliographies by topics / Microscopy – Data processing

Academic literature on the topic 'Microscopy – Data processing'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Microscopy – Data processing"

1

Rosenthaler, L., H. ‐R Hidber, A. Tonin, L. Eng, U. Staufer, R. Wiesendanger, and H. ‐J Güntherodt. "Data processing for scanning tunneling microscopy." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 6, no. 2 (March 1988): 393–97. http://dx.doi.org/10.1116/1.575402.

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Krivanek, O. L., W. J. de Ruijter, C. E. Meyer, M. L. Leber, and J. Wilbrink. "Data acquisition and processing for automated electron microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 546–47. http://dx.doi.org/10.1017/s0424820100148563.

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Automated electron microscopy promises to perform many tasks better and faster than a human operator. It should also allow the operator to concentrate on the larger picture without having to worry about countless details that can be best handled by a computer. It requires three essential components: 1) data acquisition system that provides the computer with high-quality data on line, 2) computer and software able to analyze the incoming data in real time, and 3) control links that enable the computer to adjust the important microscope parameters.An optimized system architecture is shown schematically in Fig. 1. The microscope is equipped with various microprocessors that control its hardware, and provide data processing abilities devoted to different types of signals (e.g., X-ray spectra). These microprocessors use a standardized communication protocol to communicate over a standard network (such as AppleTalk or Ethernet) with a “master computer”, which provides the user interface, as well as the computing power necessary to handle the most demanding tasks.

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Nečas, David, and Petr Klapetek. "Synthetic Data in Quantitative Scanning Probe Microscopy." Nanomaterials 11, no. 7 (July 2, 2021): 1746. http://dx.doi.org/10.3390/nano11071746.

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Synthetic data are of increasing importance in nanometrology. They can be used for development of data processing methods, analysis of uncertainties and estimation of various measurement artefacts. In this paper we review methods used for their generation and the applications of synthetic data in scanning probe microscopy, focusing on their principles, performance, and applicability. We illustrate the benefits of using synthetic data on different tasks related to development of better scanning approaches and related to estimation of reliability of data processing methods. We demonstrate how the synthetic data can be used to analyse systematic errors that are common to scanning probe microscopy methods, either related to the measurement principle or to the typical data processing paths.

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Young, P. A., A. Grislis, P. R. Barber, P. J. Keely, and K. W. Eliceiri. "Data Processing for Time-Domain Fluorescence Lifetime Imaging Microscopy." Microscopy and Microanalysis 19, S2 (August 2013): 758–59. http://dx.doi.org/10.1017/s1431927613005783.

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Rowenhorst, D. J., and A. C. Lewis. "Image processing and analysis of 3-D microscopy data." JOM 63, no. 3 (March 2011): 53–57. http://dx.doi.org/10.1007/s11837-011-0046-x.

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Sarahan, MC, QM Ramasse, and ND Browning. "Data Processing Techniques for Aberration-Corrected STEM." Microscopy and Microanalysis 16, S2 (July 2010): 112–13. http://dx.doi.org/10.1017/s1431927610055327.

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Nevernov, Igor, Marco Sartore, and Riccardo Galletti. "Object-oriented data model for scanning probe microscopy image processing." Image and Vision Computing 14, no. 6 (June 1996): 435–43. http://dx.doi.org/10.1016/0262-8856(95)01069-6.

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Hattne, Johan, Francis E. Reyes, Brent L. Nannenga, Dan Shi, M. Jason de la Cruz, Andrew G. W. Leslie, and Tamir Gonen. "MicroED data collection and processing." Acta Crystallographica Section A Foundations and Advances 71, no. 4 (July 1, 2015): 353–60. http://dx.doi.org/10.1107/s2053273315010669.

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MicroED, a method at the intersection of X-ray crystallography and electron cryo-microscopy, has rapidly progressed by exploiting advances in both fields and has already been successfully employed to determine the atomic structures of several proteins from sub-micron-sized, three-dimensional crystals. A major limiting factor in X-ray crystallography is the requirement for large and well ordered crystals. By permitting electron diffraction patterns to be collected from much smaller crystals, or even single well ordered domains of large crystals composed of several small mosaic blocks, MicroED has the potential to overcome the limiting size requirement and enable structural studies on difficult-to-crystallize samples. This communication details the steps for sample preparation, data collection and reduction necessary to obtain refined, high-resolution, three-dimensional models by MicroED, and presents some of its unique challenges.

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Cueva, P., R. Hovden, J. A. Mundy, H. L. Xin, and D. A. Muller. "New Approaches to Data Processing for Atomic Resolution EELS." Microscopy and Microanalysis 18, S2 (July 2012): 970–71. http://dx.doi.org/10.1017/s1431927612006708.

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Moeck, Peter, Taylor Bilyeu, Axel Mainzer Koenig, and Jack Straton. "Advances in Crystallographic Image Processing for Scanning Probe Microscopy." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1607. http://dx.doi.org/10.1107/s2053273314083922.

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Crystallographic image processing (CIP) is well established in the electron microscopy community, where it is used for the analysis and enhancement of high-resolution transmission electron microscope images of crystals and two-dimensional (2D) arrays of membrane proteins. The technique has recently been adapted to the processing of 2D periodic images from scanning probe microscopes (SPMs) [1]. Within this context, a procedure for the unambiguous identification of the underlying Bravais lattice of an experimental or theoretical image of a 2D periodic array of objects (e.g. molecules or atoms and their respective electron density distribution functions, ...) has been developed [2]. This procedure constitutes a partial solution to a longstanding but unresolved issue in CIP. The unresolved issue itself is the complete quantification of the deviations of 2D periodic images from the plane symmetry groups. A complete solution to this problem will allow for unambiguous decisions as to which plane symmetry best models experimental data when all systematic errors in the acquiring and processing of the image data have been accounted for at a level that systematic rest errors are negligible. Our 2D Bravais lattice identification procedure is independent of which type of microscope has been utilized for the recording of the images. It is based on classification procedures for non-disjoint models from the robotics community and is particularly useful for the correction of scanning tunneling microscope (STM) images that suffer from a blunt scanning probe tip artifact [2]. With the crystallographic processing of two molecular resolution STM images of periodic arrays of tetraphenoxyphthalocyanine on graphite, it is demonstrated how the classical CIP plane symmetry estimation procedures are augmented by our unambiguous translation symmetry identification method. We also apply CIP to an artificial SPM image that features a blunt scanning probe tip artifact, see the figure below.

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Dissertations / Theses on the topic "Microscopy – Data processing"

1

Walton, John Moorcroft. "The acquisition, analysis and processing of Scanning Auger Microscopy (SAM) and Scanning Tunneling Microscopy (STM) data." Thesis, University of York, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387184.

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Farnham, Rodrigo Bouchardet. "Processing and inpainting of sparse data as applied to atomic force microscopy imaging." California State University, Long Beach, 2013.

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Jarasch, Markus. "Interfacing a Computer to a Scanning Tunneling Microscope." PDXScholar, 1994. https://pdxscholar.library.pdx.edu/open_access_etds/5047.

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A program was written in 'C' to control the functions of an already existing Scanning Tunneling Microscope (STM). A DAS-1601 data acquisition card (from Keithley Data Acquisition) was installed together with its drivers for 'C' on a computer with a 486-DX motherboard. The computer was interfaced to the electronics of the STM. Images taken of HOPG (highly oriented pyrolitic graphite) were of a reasonable quality and showed atomic resolution.

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Xinyu, Chang. "Neuron Segmentation and Inner Structure Analysis of 3D Electron Microscopy Data." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1369834525.

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Rogers, Wendy Laurel. "A Mahalanobis-distance-based image segmentation error measure with applications in automated microscopy /." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66025.

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Angadi, Veerendra C. "Quantitative electron energy-loss spectrum data processing for hyperspectral imaging in analytical transmission electron microscopy." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20007/.

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Stein, Simon Christoph [Verfasser], Jörg [Akademischer Betreuer] [Gutachter] Enderlein, and Holger [Gutachter] Stark. "Advanced Data Processing in Super-resolution Microscopy / Simon Christoph Stein ; Gutachter: Jörg Enderlein, Holger Stark ; Betreuer: Jörg Enderlein." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2017. http://d-nb.info/1138835935/34.

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Andronov, Leonid. "Development of advanced methods for super-resolution microscopy data analysis and segmentation." Thesis, Strasbourg, 2018. http://www.theses.fr/2018STRAJ001.

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Parmi les méthodes de super-résolution, la microscopie par localisation de molécules uniques se distingue principalement par sa meilleure résolution réalisable en pratique mais aussi pour l’accès direct aux propriétés des molécules individuelles. Les données principales de la microscopie par localisation sont les coordonnées des fluorochromes, un type de données peu répandu en microscopie conventionnelle. Le développement de méthodes spéciales pour le traitement de ces données est donc nécessaire. J’ai développé les logiciels SharpViSu et ClusterViSu qui permettent d’effectuer les étapes de traitements les plus importantes, notamment une correction des dérives et des aberrations chromatiques, une sélection des événements de localisations, une reconstruction des données dans des images 2D ou dans des volumes 3D par le moyen de différentes techniques de visualisation, une estimation de la résolution à l’aide de la corrélation des anneaux de Fourier, et une segmentation à l’aide de fonctions K et L de Ripley. En plus, j’ai développé une méthode de segmentation de données de localisation en 2D et en 3D basée sur les diagrammes de Voronoï qui permet un clustering de manière automatique grâce à modélisation de bruit par les simulations Monte-Carlo. En utilisant les méthodes avancées de traitement de données, j’ai mis en évidence un clustering de la protéine CENP-A dans les régions centromériques des noyaux cellulaires et des transitions structurales de ces clusters au moment de la déposition de la CENP-A au début de la phase G1 du cycle cellulaire
Among the super-resolution methods single-molecule localization microscopy (SMLM) is remarkable not only for best practically achievable resolution but also for the direct access to properties of individual molecules. The primary data of SMLM are the coordinates of individual fluorophores, which is a relatively rare data type in fluorescence microscopy. Therefore, specially adapted methods for processing of these data have to be developed. I developed the software SharpViSu and ClusterViSu that allow for most important data processing steps, namely for correction of drift and chromatic aberrations, selection of localization events, reconstruction of data in 2D images or 3D volumes using different visualization techniques, estimation of resolution with Fourier ring correlation, and segmentation using K- and L-Ripley functions. Additionally, I developed a method for segmentation of 2D and 3D localization data based on Voronoi diagrams, which allows for automatic and unambiguous cluster analysis thanks to noise modeling with Monte-Carlo simulations. Using advanced data processing methods, I demonstrated clustering of CENP-A in the centromeric regions of the cell nucleus and structural transitions of these clusters upon the CENP-A deposition in early G1 phase of the cell cycle

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Fortier, Hélène. "AFM Indentation Measurements and Viability Tests on Drug Treated Leukemia Cells." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34345.

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A significant body of literature has reported strategies and techniques to assess the mechanical properties of biological samples such as proteins, cellular and tissue systems. Atomic force microscopy has been used to detect elasticity changes of cancer cells. However, only a few studies have provided a detailed and complete protocol of the experimental procedures and data analysis methods for non-adherent blood cancer cells. In this work, the elasticity of NB4 cells derived from acute promyelocytic leukemia (APL) was probed by AFM indentation measurements to investigate the effects of the disease on cellular biomechanics. Understanding how leukemia influences the nanomechanical properties of cells is expected to provide a better understanding of the cellular mechanisms associated to cancer, and promises to become a valuable new tool for cancer detection and staging. In this context, the quantification of the mechanical properties of APL cells requires a systematic and optimized approach for data collection and analysis, in order to generate reproducible and comparative data. This Thesis elucidates the automated data analysis process that integrates programming, force curve collection and analysis optimization to assess variations of cell elasticity in response to processing criteria. A processing algorithm was developed by using the IGOR Pro software to automatically analyze large numbers of AFM data sets in an efficient and accurate manner. In fact, since the analysis involves multiple steps that must be repeated for many individual cells, an automated and un-biased processing approach is essential to precisely determine cell elasticity. Different fitting models for extracting the Young’s modulus have been systematically applied to validate the process, and the best fitting criteria, such as the contact point location and indentation length, have been determined in order to obtain consistent results. The designed automated processing code described in this Thesis was used to correlate alterations in cellular biomechanics of cancer cells as they undergo drug treatments. In order to fully assess drug effects on NB4 cells, viability assays were first performed using Trypan Blue staining for primary insights before initiating thorough microplate fluorescence intensity readings using a LIVE/DEAD viability kit involving ethidium and calcein AM labelling components. From 0 to 24 h after treatment using 30 µM arsenic trioxide, relative live cell populations increased until 36 h. From 0 to 12 h post-treatment, relative populations of dead cells increased until 24 h post-treatment. Furthermore, a drastic drop in dead cell count has been observed between 12 and 24 h. Additionally, arsenic trioxide drug induced alterations in elasticity of NB4 cells can be correlated to the cell viability tests. With respect to cell mechanics, trapping of the non-adherent NB4 cells within fabricated SU8-10 microwell arrays, allowed consistent AFM indentation measurements up to 48 h after treatment. Results revealed an increase in cell elasticity up to 12 h post-treatment and a drastic decrease between 12 and 24 h. Furthermore, arsenic trioxide drug induced alterations in elasticity of NB4 cells can be correlated to the cell viability tests. In addition to these indentation and viability testing approaches, morphological appearances were monitored, in order to track the apoptosis process of the affected cells. Relationships found between viability and elasticity assays in conjunction with morphology alterations revealed distinguish stages of apoptosis throughout treatment. 24 h after initial treatment, most cells were observed to have burst or displayed obvious blebbing. These relations between different measurement methods may reveal a potential drug screening approach, for understanding specific physical and biological of drug effects on the cancer cells.

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Vaillancourt, Benoit. "Novel biophysical appliations [sic] of STICS." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=111550.

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The object of this thesis is to present two novel applications of Spatiotemporal Image Correlation Spectroscopy (STICS) to biological systems. STICS is a technique which uses the correlations in pixel intensity fluctuations of an image time series, captured under fluorescence microscopy, to measure the speed and direction of a flowing population of fluorescently labeled molecules. The method was first applied to measure the dynamics of transport vesicles inside growing pollen tubes of lily flowers. The measured vector maps allowed to confirm the presence of actin filaments along the periphery of the tubes, as well as the presence of a reverse-fountain pattern in the apical region. In a second set of experiments, STICS was used to measure the retrograde flow of filamentous actin in migrating chick DRG neuronal growth cones. These results serve as proof of principle that STICS can be used to probe the response of the growth cone cytoskeleton to external chemical cues.

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Books on the topic "Microscopy – Data processing"

1

Cheng, P. C. Multidimensional Microscopy. New York, NY: Springer New York, 1994.

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Russ, John C. Computer-assisted microscopy: The measurement and analysisof images. New York: Plenum Press, 1990.

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Computer operation for microscope photometry. Boca Raton, Fla: CRC Press, 1998.

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Russ, John C. Computer-assisted microscopy: The measurement and analysis of images. New York: Plenum Press, 1990.

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Sarid, Dror. Exploring scanning probe microscopy with mathematica. 2nd ed. Weinheim: Wiley-VCH, 2007.

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Koehler, James K. Advanced Techniques in Biological Electron Microscopy III. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986.

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International Metallographic Society. Technical Meeting. Computer-aided microscopy and metallography: Proceedings of the Twenty-Second Annual Technical Meeting of the International Metallographic Society. Columbus, Ohio USA: The Society, 1990.

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Gao fen zi yan jiu fang fa. Beijing Shi: Zhongguo shi hua chu ban she, 2011.

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Sarid, Dror. Exploring scanning probe microscopy with mathematica. 2nd ed. Weinheim: Wiley-VCH, 2007.

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Sarid, Dror. Exploring scanning probe microscopy with Mathematica. New York: Wiley, 1997.

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Book chapters on the topic "Microscopy – Data processing"

1

Voigtländer, Bert. "Data Representation and Image Processing." In Scanning Probe Microscopy, 107–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45240-0_7.

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Voigtländer, Bert. "Data Representation and Image Processing." In Atomic Force Microscopy, 125–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13654-3_7.

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Boxall, Eric S., Nick S. White, and Gerald S. Benham. "The Processing of Three-dimensional Confocal Data Sets." In Multidimensional Microscopy, 251–66. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4613-8366-6_14.

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Marinello, F., D. Passeri, P. Schiavuta, and E. Savio. "Data Processing for Acoustic Probe Microscopy Techniques." In Acoustic Scanning Probe Microscopy, 375–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27494-7_13.

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Mahjoubfar, Ata, Claire Lifan Chen, and Bahram Jalali. "Big Data Acquisition and Processing in Real-Time." In Artificial Intelligence in Label-free Microscopy, 67–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51448-2_7.

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Kaczmarek, Elzbieta, Aldona Wozniak, and Wieslawa Salwa-Zurawska. "Fuzzy Sets Applied to Image Processing and Quantification of Interstitial Fibrosis and Glomerular Size in Computer Assisted Microscopy." In Medical Data Analysis, 120–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45497-7_18.

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Leal-Taixé, Laura, Matthias Heydt, Axel Rosenhahn, and Bodo Rosenhahn. "Understanding What we Cannot See: Automatic Analysis of 4D Digital In-Line Holographic Microscopy Data." In Video Processing and Computational Video, 52–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24870-2_3.

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D’Antuono, Rocco. "Basic Digital Image Acquisition, Design, Processing, Analysis, Management, and Presentation." In Principles of Light Microscopy: From Basic to Advanced, 77–104. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04477-9_4.

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What You Will Learn in This ChapterUnderstand what is a single image or what a more complex multidimensional dataset represents; identify the technique used for the acquisition and read the metadata; consider the limits deriving from the imaging technique; be able to visualize and render the dataset using different software tools; apply basic image analysis workflows to get data out of images.Present images and data analysis results in an unbiased way.

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Gómez-de-Mariscal, Estibaliz, Daniel Franco-Barranco, Arrate Muñoz-Barrutia, and Ignacio Arganda-Carreras. "Building a Bioimage Analysis Workflow Using Deep Learning." In Bioimage Data Analysis Workflows ‒ Advanced Components and Methods, 59–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76394-7_4.

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AbstractThe aim of this workflow is to quantify the morphology of pancreatic stem cells lying on a 2D polystyrene substrate from phase contrast microscopy images. For this purpose, the images are first processed with a Deep Learning model trained for semantic segmentation (cell/background); next, the result is refined and individual cell instances are segmented before characterizing their morphology. Through this workflow the readers will learn the nomenclature and understand the principles of Deep Learning applied to image processing.

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Mukerji, T., and M. Prasad. "Image Processing of Acoustic Microscopy Data to Estimate Textural Scales and Anistropy in Shales." In Acoustical Imaging, 21–29. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/1-4020-5721-0_3.

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Conference papers on the topic "Microscopy – Data processing"

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Trimby, Pat. "Developing EBSD data processing software to meet the challenges of big data and process automation." In European Microscopy Congress 2020. Royal Microscopical Society, 2021. http://dx.doi.org/10.22443/rms.emc2020.663.

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Koch, Christoph. "Integrating efficient processing of large EM data sets with big data analytical tools – an operator-friendly open-source solution." In European Microscopy Congress 2020. Royal Microscopical Society, 2021. http://dx.doi.org/10.22443/rms.emc2020.1111.

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Ruane, Michael F., Sergei N. Gadetsky, and Masud Mansuripur. "Observation of domains and grooves on MO disks with optical microscopy and image processing." In Optical Data Storage '94, edited by David K. Campbell, Martin Chen, and Koichi Ogawa. SPIE, 1994. http://dx.doi.org/10.1117/12.190205.

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Lezoray, O. "Graph-based morphological processing of multivariate microscopy images and data bases." In 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490231.

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Lowerison, Matthew, Xi Chen, Chengwu Huang, Wei Zhang, Shanshan Tang, Nathiya Chandra Sekaran, Daniel Llano, Shigao Chen, and Pengfei Song. "Multi-resolution Data Processing for Accelerated and Robust Ultrasound Localization Microscopy." In 2020 IEEE International Ultrasonics Symposium (IUS). IEEE, 2020. http://dx.doi.org/10.1109/ius46767.2020.9251757.

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De Meyer, Arnaud, Bruno Colicchio, Jan De Mey, Georges Jung, Alain Dieterlen, Olivier Haeberlé, and Serge Jacquey. "Contribution of data pre-processing to deconvolution of 3D fluorescence microscopy images." In Photonics Europe, edited by Romualda Grzymala and Olivier Haeberle. SPIE, 2006. http://dx.doi.org/10.1117/12.662953.

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Vescovi, Rafael, Hanyu Li, Jeffery Kinnison, Murat Keceli, Misha Salim, Narayanan Kasthuri, Thomas D. Uram, and Nicola Ferrier. "Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data." In 2020 IEEE/ACM 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop Computing (XLOOP). IEEE, 2020. http://dx.doi.org/10.1109/xloop51963.2020.00008.

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Wise, Barry M. "Visualization of three-way and higher order data sets (Conference Presentation)." In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, edited by Thomas G. Brown and Tony Wilson. SPIE, 2019. http://dx.doi.org/10.1117/12.2516224.

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Hwang, Wonsang, Dongeun Kim, and Dugyoung Kim. "Optimal conditions for the phasor plot analysis of fluorescence lifetime data." In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXIX, edited by Thomas G. Brown, Tony Wilson, and Laura Waller. SPIE, 2022. http://dx.doi.org/10.1117/12.2608466.

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Hagiwara, Tsuneyuki. "DOF adjustment and 3D free viewpoint viewing of Z stack data of fluorescent microscope using image processing." In Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVIII, edited by Thomas G. Brown, Tony Wilson, and Laura Waller. SPIE, 2021. http://dx.doi.org/10.1117/12.2577412.

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Reports on the topic "Microscopy – Data processing"

1

Hanwell, Marcus. OPEN SOURCE PLATFORM FOR LIVE PROCESSING OF HIGH DATA RATE ELECTRON MICROSCOPY. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1602474.

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