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Journal articles on the topic 'Microscopy – Data processing'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Nefedev, Konstantin, Vitalii Y. Kapitan, and Yuriy Shevchenko. "The Inverse Task for Magnetic Force Microscopy Data." Applied Mechanics and Materials 328 (June 2013): 744–47. http://dx.doi.org/10.4028/www.scientific.net/amm.328.744.

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The computer processing of cobalt nanodots magnetic force microscopy was fulfilled. The solution of reverse task of magnetic force microscopy is obtained for surface nanosystems. Superposition of fields, which are generated by a system of magnetic moments in the selected point in space, causes a linear dependence of the force gradient of the dipole-dipole interaction between the components of the vectors.

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12

Amat, Fernando, Burkhard Höckendorf, Yinan Wan, William C. Lemon, Katie McDole, and Philipp J. Keller. "Efficient processing and analysis of large-scale light-sheet microscopy data." Nature Protocols 10, no. 11 (October 1, 2015): 1679–96. http://dx.doi.org/10.1038/nprot.2015.111.

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13

Hugelier, S., M. Sliwa, and C. Ruckebusch. "A Perspective on Data Processing in Super-resolution Fluorescence Microscopy Imaging." Journal of Analysis and Testing 2, no. 3 (July 2018): 193–209. http://dx.doi.org/10.1007/s41664-018-0076-2.

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14

Fuchs, H., W. Eustachi, and R. Seifert. "A data acquisition and image processing system for scanning tunneling microscopy." Scanning 11, no. 3 (1989): 139–46. http://dx.doi.org/10.1002/sca.4950110305.

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15

Peters, Klaus-Ruediger, and Eisaku Oho. "New digital image processing technology for FSEM microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 212–13. http://dx.doi.org/10.1017/s0424820100146904.

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Digital image acquisition and processing can provide many advantages over conventional analog image information handling, i.e., undisturbed access to the “raw data set”, quantitative analysis of the image information, and reduced costs and increased flexibility of image data handling. However, it may principally change microscopy by providing a new facility for instant exhaustive data presentation in acquired images. Detail imaging is one of the basic microscopic tasks but visual access to detail information is cumbersome and often left to post-session data analysis. A dedicated software/hardware technique is now available for automatic “near-real-time” enhancement of image detail information visually not accessible in the “raw data” image. Pertinent image details include spatial dimensions of only a few pixels in size (spatial details) and intensity variations of only a few intensity steps in height (intensity details). While conventional image enhancement techniques often produce serious image artifacts which exclude a closer inspection of enhanced detail information, the new pixel-accurate processing (PAP) technology allows instant image evaluation at an accuracy-level of the raw data through detail enhancement in full-frame images, digital zoom and noise smoothing.

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16

Newell, Bruce D. "Image processing and analysis fundamentals for microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 53 (August 13, 1995): 678–79. http://dx.doi.org/10.1017/s0424820100139767.

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Advances in computers and related digital hardware, coupled with sophisticated software techniques have resulted in microscopy migrating from its historical roots as a subjective, qualitative science towards a more robust position as a truly quantitative technique. Granted, we will probably never totally remove the microscopist from the process of image interpretation (at least those at this conference hope not) but we will certainly continue to progress from describing our image data in qualitative terms (e.g., many/few, large/small, equiaxis/elongated, ordered/random, rough/smooth) to quantitative measurements of number, size, shape, location, texture, and so on.To move along the path toward quantitative image interpretations requires an understanding of image processing and analysis (IP/A) fundamentals to insure that the data obtained is of the required accuracy and precision. A generalized model of the critical steps in the image processing and analysis chain is given in Figure 1. This tutorial will examine the fundamental issues in each step that impact the quality of the final result and provide a broad overview of techniques that may be applicable.

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17

Pichkur, Evgeny, Timur Baimukhametov, Anton Teslyuk, Anton Orekhov, Roman Kamyshinsky, Yury Chesnokov, Viacheslav Ilyin, Alexander Vasiliev, and Vasily Velikhov. "Towards on-the-fly Cryo-Electron Microscopy Data Processing by High Performance Data Analysis." Journal of Physics: Conference Series 955 (January 2018): 012005. http://dx.doi.org/10.1088/1742-6596/955/1/012005.

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18

Jones, L., and P. D. Nellist. "Post-processing of STEM Data for Instability and Drift Compensation." Microscopy and Microanalysis 18, S2 (July 2012): 1232–33. http://dx.doi.org/10.1017/s143192761200801x.

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19

Weis, Felix, and Wim J. H. Hagen. "Combining high throughput and high quality for cryo-electron microscopy data collection." Acta Crystallographica Section D Structural Biology 76, no. 8 (July 27, 2020): 724–28. http://dx.doi.org/10.1107/s2059798320008347.

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Cryo-electron microscopy (cryo-EM) can be used to elucidate the 3D structure of macromolecular complexes. Driven by technological breakthroughs in electron-microscope and electron-detector development, coupled with improved image-processing procedures, it is now possible to reach high resolution both in single-particle analysis and in cryo-electron tomography and subtomogram-averaging approaches. As a consequence, the way in which cryo-EM data are collected has changed and new challenges have arisen in terms of microscope alignment, aberration correction and imaging parameters. This review describes how high-end data collection is performed at the EMBL Heidelberg cryo-EM platform, presenting recent microscope implementations that allow an increase in throughput while maintaining aberration-free imaging and the optimization of acquisition parameters to collect high-resolution data.

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20

Dodson, T. A., T. A. Nolan, L. F. Allard, and E. Völki. "Building Data Storage Systems And Data Networks To Support Digital Microscopy." Microscopy Today 3, no. 9 (November 1995): 12–13. http://dx.doi.org/10.1017/s1551929500065792.

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As laboratories like the Materials Analysis User Center (MAUC) in the High Temperature Materials Laboratory, at Oak Ridge National Laboratory move from analog to digital imaging systems, the process for acquiring, processing, analyzing, and publishing research results is changing. In this case since original scientific data exist only in digital form, analog systems for gathering, staring, and transmitting data are being set aside in favor of digital systems. In order to adequately protect original scientific data and to ensure that digital laboratories have the same (or greater) functionality as analog laboratories, scientists must focus on building information systems that make data highly available, highly reliable, and quickly accessible. Meeting these three objectives imposes special requirements for both data storage systems and data networks.

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21

Rheinberger, Jan, Gert Oostergetel, Guenter P. Resch, and Cristina Paulino. "Optimized cryo-EM data-acquisition workflow by sample-thickness determination." Acta Crystallographica Section D Structural Biology 77, no. 5 (April 27, 2021): 565–71. http://dx.doi.org/10.1107/s205979832100334x.

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Sample thickness is a known key parameter in cryo-electron microscopy (cryo-EM) and can affect the amount of high-resolution information retained in the image. Yet, common data-acquisition approaches in single-particle cryo-EM do not take it into account. Here, it is demonstrated how the sample thickness can be determined before data acquisition, allowing the identification of optimal regions and the restriction of automated data collection to images with preserved high-resolution details. This quality-over-quantity approach almost entirely eliminates the time- and storage-consuming collection of suboptimal images, which are discarded after a recorded session or during early image processing due to a lack of high-resolution information. It maximizes the data-collection efficiency and lowers the electron-microscopy time required per data set. This strategy is especially useful if the speed of data collection is restricted by the microscope hardware and software, or if microscope access time, data transfer, data storage and computational power are a bottleneck.

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22

Chung, Jeong Min, Clarissa L. Durie, and Jinseok Lee. "Artificial Intelligence in Cryo-Electron Microscopy." Life 12, no. 8 (August 19, 2022): 1267. http://dx.doi.org/10.3390/life12081267.

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Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells.

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23

Waithe, Dominic, Falk Schneider, Jakub Chojnacki, Mathias P. Clausen, Dilip Shrestha, Jorge Bernardino de la Serna, and Christian Eggeling. "Optimized processing and analysis of conventional confocal microscopy generated scanning FCS data." Methods 140-141 (May 2018): 62–73. http://dx.doi.org/10.1016/j.ymeth.2017.09.010.

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24

Jiang, Shi-hong, and John G. Walker. "Non-scanning fluorescence confocal microscopy using speckle illumination and optical data processing." Optics Communications 256, no. 1-3 (December 2005): 35–45. http://dx.doi.org/10.1016/j.optcom.2005.06.055.

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25

Clausen, Alexander, Dieter Weber, Karina Ruzaeva, Vadim Migunov, Anand Baburajan, Abijith Bahuleyan, Jan Caron, et al. "LiberTEM: Software platform for scalable multidimensional data processing in transmission electron microscopy." Journal of Open Source Software 5, no. 50 (June 20, 2020): 2006. http://dx.doi.org/10.21105/joss.02006.

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26

Black, D. L., S. C. Stoker, and J. R. Minter. "Hybrid Imaging in Electron Microscopy." Microscopy and Microanalysis 7, S2 (August 2001): 840–41. http://dx.doi.org/10.1017/s1431927600030270.

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Imaging is an integral part of electron microscopy and is one of the mechanisms we use to share data with our colleagues. We interpret these images to understand microstructure of biological, polymeric and solid state materials. The imaging process can be viewed as a multistep process comprising image capture, processing, and storage. For example, images can be captured on film or digitally using CCD cameras. The processing step uses computers and the appropriate software and the images are stored as hard copies such as prints or a data file. Digital imaging is a dramatically evolving field that has enhanced the way we interpret and analyze these images.Although digital imaging can be used in each step of the imaging process, one can also use a hybrid system to minimize cost and retain the advantages of digital imaging. in this presentation, we discuss using a hybrid system that allows us to retain the advantages of digital processing and storage of images.

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27

Dodson, T. A., T. A. Nolan, L. F. Allard, and E. Völkl. "Building data storage systems and data networks to support digital microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 53 (August 13, 1995): 32–33. http://dx.doi.org/10.1017/s0424820100136532.

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As laboratories like the Materials Analysis User Center (MAUC) in the HTML at ORNL move from analog to digital imaging systems, the process for acquiring, processing, analyzing, and publishing research results is changing. In this case since original scientific data exist only in digital form, analog systems for gathering, storing, and transmitting data are being set aside in favor of digital systems. In order to adequately protect original scientific data and to ensure that digital laboratories have the same (or greater) functionality as analog laboratories, scientists must focus on building information systems that make data highly available, highly reliable, and quickly accessible. Meeting these three objectives imposes special requirements for both data storage systems and data networks. A data storage system for a digital microscopy laboratory must have a very large capacity (at MAUC, 30 GB). In addition, the data stored within the system must be highly available and highly reliable.

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28

Chiaramonti, Ann N., and Laurence D. Marks. "Atomic Resolution Transmission Electron Microscopy of Surfaces." Journal of Materials Research 20, no. 7 (July 1, 2005): 1619–27. http://dx.doi.org/10.1557/jmr.2005.0211.

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A brief overview of transmission electron microscopy as it applies specifically to obtaining surface crystallographic information is presented. This review will encompass many of the practical aspects of obtaining surface crystal information from a transmission electron microscope, including equipment requirements, experimental techniques, sample preparation methods, data extraction and image processing, and complimentary techniques.

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29

Faruqi, A. R., and Sriram Subramaniam. "CCD detectors in high-resolution biological electron microscopy." Quarterly Reviews of Biophysics 33, no. 1 (February 2000): 1–27. http://dx.doi.org/10.1017/s0033583500003577.

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1. Introduction 11.1 The ‘band gap’ in silicon 22. Principles of CCD detector operation 32.1 Direct detection 32.2 Electron energy conversion into light 42.3 Optical coupling: lens or fibre optics? 62.4 Readout speed and comparison with film 83. Practical considerations for electron microscopic applications 93.1 Sources of noise 93.1.1 Dark current noise 93.1.2 Readout noise 93.1.3 Spurious events due to X-rays or cosmic rays 103.2 Efficiency of detection 113.3 Spatial resolution and modulation transfer function 123.4 Interface to electron microscope 143.5 Electron diffraction applications 154. Prospects for high-resolution imaging with CCD detectors 185. Alternative technologies for electronic detection 235.1 Image plates 235.2 Hybrid pixel detectors 246. References 26During the past decade charge-coupled device (CCD) detectors have increasingly become the preferred choice of medium for recording data in the electron microscope. The CCD detector itself can be likened to a new type of television camera with superior properties, which makes it an ideal detector for recording very low exposure images. The success of CCD detectors for electron microscopy, however, also relies on a number of other factors, which include its fast response, low noise electronics, the ease of interfacing them to the electron microscope, and the improvements in computing that have made possible the storage and processing of large images.CCD detectors have already begun to be routinely used in a number of important biological applications such as tomography of cellular organelles (reviewed by Baumeister, 1999), where the resolution requirements are relatively modest. However, in most high- resolution microscopic applications, especially where the goal of the microscopy is to obtain structural information at near-atomic resolution, photographic film has continued to remain the medium of choice. With the increasing interest and demand for high-throughput structure determination of important macromolecular assemblies, it is clearly important to have tools for electronic data collection that bypass the slow and tedious process of processing images recorded on photographic film.In this review, we present an analysis of the potential of CCD-based detectors to fully replace photographic film for high-resolution electron crystallographic applications.

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30

Kozubek, Michal, Petr Matula, Pavel Matula, and Stanislav Kozubek. "Automated acquisition and processing of multidimensional image data in confocal in vivo microscopy." Microscopy Research and Technique 64, no. 2 (2004): 164–75. http://dx.doi.org/10.1002/jemt.20068.

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31

Wuhrer, R., and K. Moran. "Post Processing Hyper-Spectral Data and Generating More Information from X-ray Maps." Microscopy and Microanalysis 19, S2 (August 2013): 828–29. http://dx.doi.org/10.1017/s1431927613006132.

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32

Maluenda, D., T. Majtner, P. Horvath, J. L. Vilas, A. Jiménez-Moreno, J. Mota, E. Ramírez-Aportela, et al. "Flexible workflows for on-the-fly electron-microscopy single-particle image processing using Scipion." Acta Crystallographica Section D Structural Biology 75, no. 10 (October 1, 2019): 882–94. http://dx.doi.org/10.1107/s2059798319011860.

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Electron microscopy of macromolecular structures is an approach that is in increasing demand in the field of structural biology. The automation of image acquisition has greatly increased the potential throughput of electron microscopy. Here, the focus is on the possibilities in Scipion to implement flexible and robust image-processing workflows that allow the electron-microscope operator and the user to monitor the quality of image acquisition, assessing very simple acquisition measures or obtaining a first estimate of the initial volume, or the data resolution and heterogeneity, without any need for programming skills. These workflows can implement intelligent automatic decisions and they can warn the user of possible acquisition failures. These concepts are illustrated by analysis of the well known 2.2 Å resolution β-galactosidase data set.

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33

Xiao, Xianghui, Zhengrui Xu, Feng Lin, and Wah-Keat Lee. "TXM-Sandbox: an open-source software for transmission X-ray microscopy data analysis." Journal of Synchrotron Radiation 29, no. 1 (January 1, 2022): 266–75. http://dx.doi.org/10.1107/s1600577521011978.

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A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook. Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.

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34

Oliver, William R. "Image Processing and Scientific Misconduct." Microscopy Today 6, no. 4 (May 1998): 12–13. http://dx.doi.org/10.1017/s1551929500067213.

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In a recent microscopy discussion group, a correspondent questioned the use of a contrast enhancement method applied to a microscopy image for publication. What kinds of image processing, folk asked, are “acceptable” in a general sense? What kinds of image processing should be noted in the text or caption and what kind of images should be archived?It is an interesting set of questions, and I face them wearing three hats, I am a forensic pathologist who performs forensic image interpretation in the investigation of homicide and assault, an anatomic pathologist with an interest in confocal microscopy, and a computer scientist with training in image processing and computer vision. Image processing is part and parcel of my everyday work. I use tools such as contrast enhancement, debarring, and photogrammetry for image interpretation. I build tools for visualization in my confocal work. I collaborate in the design of data acquisition devices for the evaluation of crime scenes and bodily injury.

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35

Tietz, R. "Principles and Practice of On-Line Data Acquisition For Transmission Electron Microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 1 (August 12, 1990): 528–29. http://dx.doi.org/10.1017/s0424820100181397.

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For about 15 years now TV-camera based image acquisition systems have been used in many laboratories. These TV-systems facilitate focusing and stigmating of the instrument at low beam currents and the recording of dynamic events in the microscope with a video-tape recorder. Recently, digital image processing systems have become available which make image accumulation or averaging possible, or which can correct for uneven illumination conditions. This simple image processing is the basis for further image analysis (Automatic control of TEM parameters or real analysis of the specimen).The bottle neck in on-line data acquisition for a TEM is the image pick-up system. Compared to a photographic plate, which has about 10.000 by 10.000 resolved pixels, the resolution of commercial, TV-based camera systems is very poor, in the best case about 500 by 500 pixels. The poor resolution of TV-systems restricts the use of image analysis to objects which do not need large image fields.Fig. 1 illustrates the principles of TV-based image pick-up systems.

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36

Perdigão, Luís M. A., Neville B. y. Yee, Elaine M. L. Ho, Avery H. Pennington, Oliver N. F. King, Michele C. Darrow, and Mark Basham. "Computing for Optimized Biological Microscopy Data Processing and Analysis at The Rosalind Franklin Institute." Microscopy and Microanalysis 28, S1 (July 22, 2022): 1462–64. http://dx.doi.org/10.1017/s1431927622005931.

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37

Huber, Daniel E., John M. Sosa, Jim Riedel, Chas D. Ellerbrock, and D. M. McComb. "Implementing High Performance Workstation Virtualization for Data Processing in a Multi-User Microscopy Facility." Microscopy and Microanalysis 23, S1 (July 2017): 1070–71. http://dx.doi.org/10.1017/s1431927617006018.

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38

Sungur, E., G. Taupier, O. Crégut, D. Gindre, L. Pieuchot, J. L. Evrard, A. C. Schmit, L. Mager, and K. D. Dorkenoo. "Multiphotonic microscopy as a processing tool for optical data storage and imaging in biophysics." Annales de Physique 32, no. 2-3 (2007): 147–49. http://dx.doi.org/10.1051/anphys:2008029.

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39

Beverte, Ilze. "Determination of highly porous plastic foam structural characteristics by processing light microscopy images data." Journal of Applied Polymer Science 131, no. 4 (July 3, 2013): n/a. http://dx.doi.org/10.1002/app.39477.

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Edbert, Daniel, Ni Made Mertaniasih, Pepy Dwi Endraswari, and Eko Budi Koendhori. "Light wave filtration by colored cellophane to optimize microscopic contrast ratio in pulmonary tuberculosis sputum observation." F1000Research 11 (March 1, 2022): 254. http://dx.doi.org/10.12688/f1000research.109146.1.

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Introduction: Indonesia has the second highest number of Tuberculosis (TB) in the world and tuberculosis still pose as a global health priority problem. For the diagnostics, microscopy is still one of important modalities in TB diagnosis especially in peripheral areas for screening, case-finding, and treatment evaluation. The use of microscopy is limited to operator visual burden and specimen load. This study aims to modify the use of the microscope by using everyday item to increase microscope operator workload. Method: This is an analytic study of the use of colored cellophane in microscopic examination of sputum smear from 24 pulmonary TB patients from Jakarta and Surabaya. The sputum samples were confirmed positive by standardized ZN-AFB microscopy and/or Xpert MTB/RIF. Each sputum were made into two slides, for each slide, 10 oil immersion fields were captured using Optilab microscope camera mounted on microscope. A total of 480 data pairs were analysed by comparing color characteristic of acid-fast bacilli pixels and its adjacent background pixels and by counting Acid fast Bacilli for each observation field. IUATLD scale were also observed by 3 operators. Results: Contrast ratio for each AFB and its adjacent pixels were significantly higher in groups using cellophane filter than without using filter (p = 0.001). AFB identification per image acquired using filter were not significantly higher (p=0.815). Conclusion: Colored cellophane increases contrast ratio and eases microscopic examination, thus increases sputum smear microscopic observation capability. Also it might increase identification capability with digital image processing.

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Penczek, P., Ch M. T. Spahn, R. K. Agrawal, I. S. Gabashvili, R. A. Grassucci, and J. Frank. "Advances in High Resolution Cryo-EM Studies: the Quartenary Structure of the 70S E.Coli Ribosome Revealed at 11.5Å Resolution." Microscopy and Microanalysis 6, S2 (August 2000): 262–63. http://dx.doi.org/10.1017/s1431927600033808.

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Three-dimensional cryo-electron microscopy (EM) of macromolecular assemblies with low or nonexistent symmetry in single-particle form is now recognized as a powerful, legitimate method of structure research. The progress in the resolution achieved has always been related to the introduction of new image processing methods and to improvements in understanding of the overall strategy of single particle analysis. The new techniques either were adopted from methods developed in other fields, or were specifically designed to deal with extremely low Signal-to-Noise Ratio (SNR) data in EM. In the former category we can place multivariate statistical analysis, clustering techniques, correlation techniques, and tomographic methods, in the latter alignment methods, three-dimensional (3D) projection alignment, contrast transfer function (CTF) correction methods, and common-lines based orientational search. In addition, there have been steady improvements of the quality of the data by seeking better electron microscopy conditions (usage of higher microscope voltage and of microscopes equipped with a field emission gun).

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Wells, Wendy A., Michael Thrall, Anastasia Sorokina, Jeffrey Fine, Savitri Krishnamurthy, Attiya Haroon, Babar Rao, et al. "In Vivo and Ex Vivo Microscopy: Moving Toward the Integration of Optical Imaging Technologies Into Pathology Practice." Archives of Pathology & Laboratory Medicine 143, no. 3 (December 10, 2018): 288–98. http://dx.doi.org/10.5858/arpa.2018-0298-ra.

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The traditional surgical pathology assessment requires tissue to be removed from the patient, then processed, sectioned, stained, and interpreted by a pathologist using a light microscope. Today, an array of alternate optical imaging technologies allow tissue to be viewed at high resolution, in real time, without the need for processing, fixation, freezing, or staining. Optical imaging can be done in living patients without tissue removal, termed in vivo microscopy, or also in freshly excised tissue, termed ex vivo microscopy. Both in vivo and ex vivo microscopy have tremendous potential for clinical impact in a wide variety of applications. However, in order for these technologies to enter mainstream clinical care, an expert will be required to assess and interpret the imaging data. The optical images generated from these imaging techniques are often similar to the light microscopic images that pathologists already have expertise in interpreting. Other clinical specialists do not have this same expertise in microscopy, therefore, pathologists are a logical choice to step into the developing role of microscopic imaging expert. Here, we review the emerging technologies of in vivo and ex vivo microscopy in terms of the technical aspects and potential clinical applications. We also discuss why pathologists are essential to the successful clinical adoption of such technologies and the educational resources available to help them step into this emerging role.

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Eschweiler, Dennis, Malte Rethwisch, Mareike Jarchow, Simon Koppers, and Johannes Stegmaier. "3D fluorescence microscopy data synthesis for segmentation and benchmarking." PLOS ONE 16, no. 12 (December 2, 2021): e0260509. http://dx.doi.org/10.1371/journal.pone.0260509.

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Automated image processing approaches are indispensable for many biomedical experiments and help to cope with the increasing amount of microscopy image data in a fast and reproducible way. Especially state-of-the-art deep learning-based approaches most often require large amounts of annotated training data to produce accurate and generalist outputs, but they are often compromised by the general lack of those annotated data sets. In this work, we propose how conditional generative adversarial networks can be utilized to generate realistic image data for 3D fluorescence microscopy from annotation masks of 3D cellular structures. In combination with mask simulation approaches, we demonstrate the generation of fully-annotated 3D microscopy data sets that we make publicly available for training or benchmarking. An additional positional conditioning of the cellular structures enables the reconstruction of position-dependent intensity characteristics and allows to generate image data of different quality levels. A patch-wise working principle and a subsequent full-size reassemble strategy is used to generate image data of arbitrary size and different organisms. We present this as a proof-of-concept for the automated generation of fully-annotated training data sets requiring only a minimum of manual interaction to alleviate the need of manual annotations.

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MAURER, MAURICIO RAFAEL, HELIO PEDRINI, and MARCO ANTONIO FERREIRA RANDI. "PROCESSING AND VISUALIZATION OF LIGHT MICROSCOPE IMAGES." International Journal of Image and Graphics 09, no. 03 (July 2009): 369–88. http://dx.doi.org/10.1142/s0219467809003484.

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The analysis of three-dimensional structures of tissues and cellular constituents is a fundamental task in Biology and Medicine. Although three-dimensional images, acquired by light microscopes, play an important role in such knowledge domains, their analysis has not been much exploited compared to other imaging technologies, such as X-ray radiography, computerized tomography or magnetic resonance. In light microscopy, the majority of the activities involved in the image analysis (for instance, detection, counting, quantification) is still performed manually. The main difficulties among the others include the fact that the objects under investigation usually have complex structures, large number of cellular elements, shape variations and presence of noise in the acquired images. This paper describes a method for processing and visualization of images obtained with light microscopes. An effective transfer function based on the optical density of the cellular constituents is employed to generate the volumetric visualization. Several real data sets are used to demonstrate the effectiveness of the proposed method.

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Wakonig, Klaus, Hans-Christian Stadler, Michal Odstrčil, Esther H. R. Tsai, Ana Diaz, Mirko Holler, Ivan Usov, Jörg Raabe, Andreas Menzel, and Manuel Guizar-Sicairos. "PtychoShelves, a versatile high-level framework for high-performance analysis of ptychographic data." Journal of Applied Crystallography 53, no. 2 (March 13, 2020): 574–86. http://dx.doi.org/10.1107/s1600576720001776.

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Over the past decade, ptychography has been proven to be a robust tool for non-destructive high-resolution quantitative electron, X-ray and optical microscopy. It allows for quantitative reconstruction of the specimen's transmissivity, as well as recovery of the illuminating wavefront. Additionally, various algorithms have been developed to account for systematic errors and improved convergence. With fast ptychographic microscopes and more advanced algorithms, both the complexity of the reconstruction task and the data volume increase significantly. PtychoShelves is a software package which combines high-level modularity for easy and fast changes to the data-processing pipeline, and high-performance computing on CPUs and GPUs.

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Yaminsky, Igor, and Assel Akhmetova. "Atomi c-force microscopy of viruses and bacteria." Medicina i vysokie tehnologii 2 (February 2021): 18–21. http://dx.doi.org/10.34219/2306-3645-2021-11-2-18-21.

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The article is devoted to the study of viruses and bacteria using a scanning probe microscope in atomic force mode, in particular, to the features of sample preparation, interpretation of the data obtained, and image processing.

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Erickson, Blake W., Séverine Coquoz, Jonathan D. Adams, Daniel J. Burns, and Georg E. Fantner. "Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing." Beilstein Journal of Nanotechnology 3 (November 13, 2012): 747–58. http://dx.doi.org/10.3762/bjnano.3.84.

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Modern high-speed atomic force microscopes generate significant quantities of data in a short amount of time. Each image in the sequence has to be processed quickly and accurately in order to obtain a true representation of the sample and its changes over time. This paper presents an automated, adaptive algorithm for the required processing of AFM images. The algorithm adaptively corrects for both common one-dimensional distortions as well as the most common two-dimensional distortions. This method uses an iterative thresholded processing algorithm for rapid and accurate separation of background and surface topography. This separation prevents artificial bias from topographic features and ensures the best possible coherence between the different images in a sequence. This method is equally applicable to all channels of AFM data, and can process images in seconds.

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Fischione, P. E., J. Ringnalda, Y. Feng, T. Krekels, H. O. Colijn, M. J. Mills, and J. M. Wiezorek. "The Use of a Cold Gas Plasma for the Final Processing of Contamination-Free TEM Specimens." Microscopy and Microanalysis 3, S2 (August 1997): 985–86. http://dx.doi.org/10.1017/s1431927600011818.

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The issue of specimen contamination becomes more important at a rate proportional to the use of high-brightness electron source Transmission Electron Microscopes (TEM). These TEMs combine smaller electron probes with increased current, allowing high resolution specimen imaging and enhanced analytical data collection. Small, high current electron probes tend to increase the detrimental effect of hydrocarbon presence on the specimen's surface. The subsequent formation of carbon deposits caused by the focused probe often times obstructs imaging and precludes acceptable analytical results. by plasma cleaning the specimen, contamination is removed and the results obtained by high resolution electron microscopy (HREM), scanning transmission electron microscopy (STEM) and analytical electron microscopy (AEM) using EDS or electron energy loss spectroscopy (EELS) are greatly enhanced. Recent instrumentation developments have resulted in the application of a high frequency, low energy, reactive gas plasma generated in an oil-free vacuum that chemically removes hydrocarbon contamination from both the TEM specimen holder and the specimen without altering its properties.

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Stoll, E. P. "Picture processing and three-dimensional visualization of data from scanning tunneling and atomic force microscopy." IBM Journal of Research and Development 35, no. 1.2 (January 1991): 67–77. http://dx.doi.org/10.1147/rd.351.0067.

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BORRETT, S., and L. HUGHES. "Reporting methods for processing and analysis of data from serial block face scanning electron microscopy." Journal of Microscopy 263, no. 1 (January 21, 2016): 3–9. http://dx.doi.org/10.1111/jmi.12377.

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