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).

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.

A practical implementation of acoustic full waveform inversion on graphical processing units

2015 ◽  
Vol 6 (2) ◽  
pp. 5-16 ◽  
Author(s):  
Sergio Alberto Abreo Carrillo ◽  
Ana B. Ramirez ◽  
Oscar Reyes ◽  
David Leonardo Abreo-Carrillo ◽  
Herling González Alvarez
Keyword(s):  
Waveform Inversion ◽  
Practical Implementation ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Graphical Processing Units ◽  
Graphical Processing

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 Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

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.

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