scholarly journals Effective image visualization for publications – a workflow using open access tools and concepts

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 1373
Author(s):  
Christopher Schmied ◽  
Helena Klara Jambor
Keyword(s):  
Image Processing ◽  
Wide Distribution ◽  
X Rays ◽  
Software Packages ◽  
Image Presentation ◽  
Graduate Curricula ◽  
Novice Users ◽  
Biomedical Publications ◽  
Processing Steps ◽  
Microscopy Images

Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization.  Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible “cheat sheet”-style format, enabling wide distribution, use, and adoption to more specific needs.

scholarly journals Effective image visualization for publications – a workflow using open access tools and concepts

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1373
Author(s):  
Christopher Schmied ◽  
Helena Klara Jambor
Keyword(s):  
Image Processing ◽  
Wide Distribution ◽  
X Rays ◽  
Software Packages ◽  
Image Presentation ◽  
Graduate Curricula ◽  
Novice Users ◽  
Biomedical Publications ◽  
Processing Steps ◽  
Microscopy Images

Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization.  Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible “cheat sheet”-style format, enabling wide distribution, use, and adoption to more specific needs.

scholarly journals Evolutionary optimization of image processing for cell detection in microscopy images

2020 ◽  
Vol 24 (23) ◽  
pp. 17847-17862
Author(s):  
Andreas Haghofer ◽  
Sebastian Dorl ◽  
Andre Oszwald ◽  
Johannes Breuss ◽  
Jaroslaw Jacak ◽  
...  
Keyword(s):  
Image Processing ◽  
Domain Knowledge ◽  
Cell Detection ◽  
Graphical Processing Units ◽  
High Scalability ◽  
Good Cell ◽  
Graphical Processing ◽  
Network Approaches ◽  
Processing Steps ◽  
Microscopy Images

AbstractIn this paper, we present a new evolution-based algorithm that optimizes cell detection image processing workflows in a self-adaptive fashion. We use evolution strategies to optimize the parameters for all steps of the image processing pipeline and improve cell detection results. The algorithm reliably produces good cell detection results without the need for extensive domain knowledge. Our algorithm also needs no labeled data to produce good cell detection results compared to the state-of-the-art neural network approaches. Furthermore, the algorithm can easily be adapted to different applications by modifying the processing steps in the pipeline and has high scalability since it supports multithreading and computation on graphical processing units (GPUs).

A Color Coded STEM/SEM Image Storage System

1981 ◽  
Vol 39 ◽  
pp. 272-273
Author(s):  
Y. Kokubo ◽  
W. H. Hardy ◽  
J. Dance ◽  
K. Jones
Keyword(s):  
Image Processing ◽  
Electron Beam ◽  
Storage System ◽  
Particle Analysis ◽  
Specific Information ◽  
X Rays ◽  
Sem Image ◽  
Backscattered Electrons ◽  
Wide Range ◽  
Scanning Electron

A color coded digital image processing is accomplished by using JEM100CX TEM SCAN and ORTEC’s LSI-11 computer based multi-channel analyzer (EEDS-II-System III) for image analysis and display. Color coding of the recorded image enables enhanced visualization of the image using mathematical techniques such as compression, gray scale expansion, gamma-processing, filtering, etc., without subjecting the sample to further electron beam irradiation once images have been stored in the memory.The powerful combination between a scanning electron microscope and computer is starting to be widely used 1) - 4) for the purpose of image processing and particle analysis. Especially, in scanning electron microscopy it is possible to get all information resulting from the interactions between the electron beam and specimen materials, by using different detectors for signals such as secondary electron, backscattered electrons, elastic scattered electrons, inelastic scattered electrons, un-scattered electrons, X-rays, etc., each of which contains specific information arising from their physical origin, study of a wide range of effects becomes possible.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

scholarly journals Improving Image Quality and Reducing Drift Problems via Automated Data Acquisition and Averaging in a Cs-Corrected TEM

2008 ◽  
Vol 16 (6) ◽  
pp. 36-39 ◽  
Author(s):  
E. Voelkl ◽  
B. Jiang ◽  
Z.R. Dai ◽  
J.P Bradley
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Data Acquisition ◽  
Information Content ◽  
Exposure Time ◽  
Image Acquisition ◽  
Ccd Camera ◽  
Automated Data Acquisition ◽  
Processing Steps

Image acquisition with a CCD camera is a single-press-button activity: after selecting exposure time and adjusting illumination, a button is pressed and the acquired image is perceived as the final, unmodified proof of what was seen in the microscope. Thus it is generally assumed that the image processing steps of e.g., “darkcurrent correction” and “gain normalization” do not alter the information content of the image, but rather eliminate unwanted artifacts.

scholarly journals Using Relion software within Scipion framework

2020 ◽  
Author(s):  
Grigory Sharov ◽  
Dustin R. Morado ◽  
Marta Carroni ◽  
José Miguel de la Rosa-Trevín
Keyword(s):  
Image Processing ◽  
User Interfaces ◽  
Published Data ◽  
New Developments ◽  
Processing Pipeline ◽  
Software Packages ◽  
File Formats ◽  
Processing Framework ◽  
Taking Care

Scipion is a modular image processing framework integrating several software packages under a unified interface while taking care of file formats and conversions. Here new developments and capabilities of the Scipion plugin for the Relion software are presented and illustrated with the image processing pipeline of published data. The user interfaces of Scipion and Relion are compared and the key differences highlighted, allowing this manuscript to be used as a guide for both new and experienced users of these software. Different streaming image processing options are also discussed demonstrating the flexibility of the Scipion framework.SynopsisAn overview of the Scipion plugin for the Relion software is presented and various capabilities of image processing within Scipion framework are discussed.

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