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187 results for “digital imaging”
Digital image correlation measurement of linear elastic steel specimen
<p>The dataset comprises the axial and lateral displacements on the surface of a plate with a hole subjected to tensile load. The displacement data are measured by digital image correlation and the material is assumed to behave linear elastic. The material under investigation is a common low-carbon steel alloy of type S235. The displacement data are used for calibration of a linear elastic constitutive model using parametric physics-informed neural networks and finite elements. For that purpose, the dataset comprises both the raw experimental displacement data and displacement data interpolated onto a regular grid using linear interpolation, where the interpolation routine is provided as well.</p>
Artefact segmentation in digital pathology whole-slide images
<p>Dataset with examples of Artefacts in Digital Pathology.</p> <p>The dataset contains 22 Whole-Slide Images, with H&E or IHC staining, showing various types and levels of defect to the slides. Annotations were made by a biomedical engineer based on examples given by an expert.</p> <p>The dataset is split in different folders:</p> <ul> <li>train <ul> <li>18 whole-slide images (extracted at 1.25x & 2.5x magnification)</li> <li>All from the same Block (colorectal cancer tissue)</li> <li>1/2 with H&E & 1/2 with anti-pan-cytokeratin IHC staining.</li> </ul> </li> <li>validation <ul> <li>3 whole-slide images (1.25x + 2.5x mag)</li> <li>2 from the same Block as the training set (1 IHC, 1 H&E)</li> <li>1 from another Block (IHC anti-pan-cytokerating, gastroesophageal junction lesion)</li> </ul> </li> <li>validation_tiles <ul> <li>patches of varying sizes taken from the 3 validation whole-slide images @1.25x magnification.</li> <li>7 patches from each slide.</li> </ul> </li> <li>test <ul> <li>1 whole-slide image (1.25x + 2.5x mag)</li> <li>From another block: IHC staining (anti-NR2F2), mouth cancer</li> </ul> </li> </ul> <p>For the train, validation and test whole-slide images, each slide has:<br> - The RGB images @1.25x & 2.5x mag<br> - The corresponding background/tissue masks<br> - The corresponding annotation masks containing examples of artefacts (note that a majority of artefacts are not annotated. In total, 918 artefacts are in the train set)</p> <p>For the validation tiles, the following table gives the "patch-level" supervision:</p> <p>tile# Artefact(s)<br> 00 None/Few<br> 01 Tear&Fold<br> 02 Ink<br> 03 None/Few<br> 04 None/Few<br> 05 Tear&Fold<br> 06 Tear&Fold + Blur<br> 07 Knife damage<br> 08 Knife damage<br> 09 Ink<br> 10 None/Few<br> 11 Tear&Fold<br> 12 Tear&Fold<br> 13 None/Few<br> 14 None/Few<br> 15 Knife damage<br> 16 Tear&Fold<br> 17 None/Few<br> 18 None/Few<br> 19 Blur<br> 20 Knife damage</p>
Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer
<p>This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the <span>Laboratoire de Glaciologie (GLACIOL)</span> at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf). </p>
Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"
<p>Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat 15: 791015. https://doi.org/10.3389/fnana.2021.791015</p> <p>There are 2 files in this upload:</p> <p>1. The file named "<strong>2022_Liu_FrontNeuroanat.pdf</strong>" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy.</p> <p>2. The file named "<strong>Liu_data_code.zip</strong>" (~1 GB) is a zipped version of a folder ‘<em>Liu_data_code</em>’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.</p> <p> </p>
Digital Repository of Ireland Member Digitisation Workflows for 2D Image Files: Survey Questions and Dataset
<p>The Digital Repository of Ireland (DRI) issued a survey to its membership, <strong>DRI Member Digitisation Workflows for 2D Images</strong>, which ran from December 7, 2023–January 31, 2024. The survey was conducted to improve the DRI’s understanding of the technical processes and metadata workflows that our members use to digitise and share images in the Repository, in order to better tailor our support for this work and deliver the most complete information about digital images files available to our users. </p> <p>The survey informed the actions taken in WorldFAIR Project WP13 deliverable <a href="https://doi.org/10.5281/zenodo.10850009" target="_blank" rel="noopener">13.3 Implementing and Testing the Cultural Heritage Image Sharing Recommendations: DRI Case Study Report</a>. The data will inform ongoing work at DRI aimed at improving the transparency of technical information associated with digital assets accessed through the Repository.</p> <p>Read more about the Cultural Heritage Image Sharing Case Study DRI on our website: <a href="https://dri.ie/the-worldfair-project/">https://dri.ie/the-worldfair-project/</a>. </p> <p>Summary: DRI is Ireland's national repository for the arts, humanities, and social sciences data, and operates on a membership scheme. There were 20 respondents to the survey, giving us a response rate of about 35% of DRI's membership. Representation from professional fields of work across the cultural heritage sector was captured in the results (note that some institutions gave multiple responses): 17 Archives, 12 Libraries, 5 Museums and 11 Higher Education Institutions. </p>
African Red Slip Ware Digital (ARS3D) - Images (Features)
<p>Characteristic of the North African bowls, plates, and jugs are their pictorial decorations applied mainly by appliqués and stamps. As mass-produced image carriers and everyday objects, the ARS spread throughout the empire.</p> <p>The range of motifs includes mythological scenes as well as scenes from the Old and New Testament, circus, arena and hunting scenes as well as fish and plant motifs. The appliqués-decorated pottery thus provides insights into Late Antique imagination and its changes, as well as into the economic history of the period between the 3rd and 5th centuries AD in North Africa.</p> <p>Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.</p> <p>Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.</p>
African Red Slip Ware Digital (ARS3D) - Images (Objects)
<p>Characteristic of the North African bowls, plates, and jugs are their pictorial decorations applied mainly by appliqués and stamps. As mass-produced image carriers and everyday objects, the ARS spread throughout the empire.</p> <p>The range of motifs includes mythological scenes as well as scenes from the Old and New Testament, circus, arena and hunting scenes as well as fish and plant motifs. The appliqués-decorated pottery thus provides insights into Late Antique imagination and its changes, as well as into the economic history of the period between the 3rd and 5th centuries AD in North Africa.</p> <p>Previous documentation methods were not able to capture the objects and their decoration in an adequate way. The digital recording of the RGZM's collections by 3D scans allows to compare potentially identical appliqués and to assign them to their negative forms and the corresponding stamps.</p> <p>Whereas vessel curvature previously falsified the assignment of appliqués and models, 3D analysis and visualisation tools now allow a comparison . Metadata created for each object increases the effectiveness and accuracy of determining image context and content. Issues related to the production of the ARS and the process flows within the workshops can be investigated through the analysis of the 3D data.</p>
Cellpose model for Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose model for Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose model, trained via transfer learning from ‘cyto’ model.</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast) and Manual annotations (<em>10.5281/zenodo.5996883</em>)</p> <p><strong>Training Procedure: </strong>Model was trained using a Cellpose version 0.6.5 with GPU support (NVIDIA GeForce RTX 2080) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> </p> <p>python -m cellpose --train --dir <em>TRAINING/DATASET/PATH/</em>train --test_dir <em>TRAINING/DATASET/PATH/</em>test --pretrained_model cyto --chan 0 --chan2 0</p> <p>The model file (MODEL NAME) in this repository is the result of this training.</p> <p><strong>Prediction Procedure: </strong>Using this model, a label image can be obtained from new unseen images in a given folder with</p> <p>python -m cellpose --dir <em>NEW/DATASET/PATH</em> --pretrained_model <em>FULL_MODEL_PATH</em> --chan 0 --chan2 0 --save_tif --no_npy</p>
Cellpose models for Label Prediction from Brightfield and Digital Phase Contrast images
<p><strong>Name: </strong>Cellpose models for Brightfield and Digital Phase Contrast images</p> <p><strong>Data type: </strong>Cellpose models trained via transfer learning from the ‘nuclei’ and ‘cyto2’ pretrained model with additional <strong>Training Dataset . Includes</strong> corresponding csv files with 'Quality Control' metrics(§) (model.zip).</p> <p><strong>Training Dataset: </strong>Light microscopy (Digital Phase Contrast or Brightfield) and automatic annotations (nuclei or cyto) (<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>)</p> <p><strong>Training Procedure: </strong>The cellpose models were trained using cellpose version 1.0.0 with GPU support (NVIDIA GeForce K40) using default settings as per the <a href="https://cellpose.readthedocs.io/en/latest/train.html">Cellpose documentation</a> . Training was done using a <a href="https://datascience.ch/renku/">Renku </a>environment (<a href="https://github.com/BIOP/renku-templates/tree/main/VNC-Napari-Fiji-Omero-CUDA11.4-cellpose-omnipose">renku template</a>).</p> <p> </p> <p><strong>Command Line Execution for the different trained models</strong></p> <p><strong>nuclei_from_bf: </strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _bf --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_bf</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _bf --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_dpc:</strong></p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _dpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_dpc</strong>:</p> <pre><code>cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _dpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><strong>nuclei_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model nuclei --img_filter _sqrdpc --mask_filter _nuclei --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p><strong>cyto_from_sqrdpc</strong>:</p> <pre><code class="language-python">cellpose --train --dir 'data/train/' --test_dir 'data/test/' --pretrained_model cyto2 --img_filter _sqrdpc --mask_filter _cyto --chan 0 --chan2 0 --use_gpu --verbose</code></pre> <p> </p> <p><em><strong>NOTE </strong></em>(§): We provide a notebook for Quality Control, which is an adaptation of the <a href="https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks/Beta%20notebooks/Cellpose_2D_ZeroCostDL4Mic.ipynb">"Cellpose (2D and 3D)" notebook from ZeroCostDL4Mic</a> .</p> <p><em><strong>NOTE</strong></em>: This dataset used a training dataset from the Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6140064">https://doi.org/10.5281/zenodo.6140064</a>) generated from the “HeLa “Kyoto” cells under the scope” dataset Zenodo entry(<a href="https://doi.org/10.5281/zenodo.6139958">https://doi.org/10.5281/zenodo.6139958</a>) in order to automatically generate the label images.</p> <p><strong><em>NOTE</em></strong>:<strong> </strong>Make sure that you delete the “_flow” images that are auto-computed when running the training. If you do not, then the flows from previous runs will be used for the new training, which might yield confusing results.</p> <p> </p>
Digital Elevation Model from Pléiades stereo images - Upper Tuolumne basin, California, 2017-08-13
<p><br> Digital Elevation Model (DEM) derived from a triplet of stereo images of the Pléiades satellite.<br> The images were acquired on 13th August 2017 and cover a part of the upper Tuolumne basin (California).<br> The DEM was calculated at a 3 m resolution with the Ames Stereo Pipeline software (Beyer et al., 2018).<br> Details about the processing of the images and the accuracy of the DEM are given in Deschamps-Berger et al. (2020).</p> <p> </p> <p>References<br> Beyer, R. A., Alexandrov, O., and McMichael, S. (2018). The Ames Stereo Pipeline: NASA’s open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537–548. https://doi.org/10.1029/2018EA00040</p> <p>Deschamps-Berger, C., Gascoin, S., Berthier,<br> E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M. (2020).<br> Snow depth mapping from stereo satellite imagery in mountainous terrain :<br> evaluation using airborne lidar data. <em>The Cryosphere</em>, (February):1–28. https://doi.org/10.5194/tc-14-2925-2020</p>
Digital Pediatric Image Quality Phantoms and Simulations
<p>Pediatric IQ Phantoms is a dataset of virtual phantoms and their computed tomography (CT) images for assessing the image quality of image-based CT denoising products in pediatric patients. The phantoms in the dataset enable assessment of <a title="IEC Standard 61223-5-3" href="https://webstore.iec.ch/publication/59789" target="_blank" rel="noopener">standard measures of image quality</a> – CT number accuracy, noise magnitude, uniformity, contrast dependent spatial resolution, and low contrast detectability. These phantoms are designed to span the range of pediatric effective waist diameters (Table 1) and thus can be used to assess pediatric image quality performance.</p> Table 1: Pediatric subgroups investigated defined by <table><tbody> <tr> <td><strong>Subgroup</strong></td> <td><strong>Age Range</strong></td> <td><strong>Waist Diameter Range</strong></td> </tr> <tr> <td>Newborn</td> <td>≤ 1 mo</td> <td>≤ 11.5 cm</td> </tr> <tr> <td>Infant</td> <td>> 1 mo & < 2 yrs</td> <td>> 11.5 cm & ≤ 16.8 cm</td> </tr> <tr> <td>Child</td> <td>> 2 yrs & ≤ 12 yrs</td> <td>> 16.8 cm & ≤ 23.2 cm</td> </tr> <tr> <td>Adolescent</td> <td>> 12 yrs & < 21 yrs</td> <td>> 23.2 cm & < 34 cm</td> </tr> <tr> <td>Adult</td> <td>≥ 22 yrs</td> <td>≥ 34 cm</td> </tr> </tbody> </table> <p>These phantoms include:</p> <ul> <li>CTP404 multi-contrast phantom: for assessing CT number accuracy and contrast-dependent spatial resolution. <ul> <li>CTP404 is a modified version of the sensitometry module CTP404 from the Catphan 600 phantom (The Phantom Laboratory, Salem, NY).</li> <li>This cylindrical phantom has eight unique contrast inserts ranging from -1000 to +900 HU in a uniform background of 0 HU. In its standard size, CTP404 has a diameter of 150 mm with 12 mm diameter inserts. Due to the sharp intersection between the phantom background and multi-contrast inserts, this module was used to evaluate contrast-dependent image sharpness using the contrast-dependent modulation transfer function.2</li> </ul> </li> <li>MITA LCD phantom: for assessing low contrast detectability. <ul> <li>The MITA-LCD phantom is a cylindrical phantom filled with water-equivalent attenuation material and contains four low-contrast disk inserts of different size and contrast combinations: 3mm-14HU, 5mm-7HU, 7mm-5HU, 10mm-3HU.3,4</li> </ul> </li> <li>Uniform water phantom: for assessing noise and noise texture. <ul> <li>The uniform water phantom is a cylindrical phantom filled with water-equivalent attenuation material.</li> </ul> </li> </ul>
Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>This paper has described a scheme for digital watermarking of still images based on discrete<br> wavelet transform. In the proposed method, the embedded logo watermark can be extracted without<br> access to the original image. It has been confirmed that the proposed watermarking method is able<br> to extract the embedded logo watermark from the watermarked images that have degraded through<br> compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation,<br> it can completely extract the watermark from watermarked images that lose about 35% of their<br> areas by cropping attack.</p>
Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep<br> the capacity of watermarking fixed.<br> When a region of the watermarked image is destroyed; the whole watermark can be<br> extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1<br> shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000<br> algorithm) watermarked image.</p>
Figure 4. (a) The original "Hookah" image (b) Watermarked "Hookah" with Q=35 (c) The original "Baby" image (d) Watermarked "Baby" with Q=35-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>A set of distortions is applied to the watermarked image and the watermark is extracted from<br> the distorted image. We used bit correct rate (BCR) to evaluate our proposed algorithm and it is<br> calculated from the following equation [6].</p>
Figure 2. LL2 sub-band is divided into sub-block-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme
<p>In the following experiments, two gray-level images with size of 512 by 512, “Baby” and<br> “Hookah” are the test images. The binary image “IAU” with size of 32 by 32 is used in our<br> simulations as a watermark. Figure 3 shows the watermark. In the experiments Haar wavelet filter<br> was used for discrete wavelet transform. The level of wavelet decomposition (n) and the number of<br> sub-blocks (K) were also assumed to be 2 and 16 respectively.<br> The proposed watermarking algorithm is evaluated from the point view of embedded<br> watermark transparency and robustness; the result of each is shown in next two sections.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 3. Data compression and image reconstruction (55:148 Digital Image Processing, 2017)
<p>There are several techniques which are normally divided into two categories lossy and lossless image compressions. In lossy compression, after recovery there are negligible difference present where lossless gives accurate image. Huffman encoding is very well known, which can provide optimal compression and decompression without error (55:148 Digital Image Processing, 2017). The basic idea of Huffman coding is to represent data by number of variable size, where more frequent info being represented by shorter number (55:148 Digital Image Processing, 2017). Currently the Lempel-Ziv (or Lempel-Ziv-Welch, LZW) algorithm for dictionary-based coding has got attention as a better compression algorithm (55:148 Digital Image Processing, 2017).</p>
Figure 4 in Using digital images in the study of fluctuating asymmetry in the spur-thighed tortoise Testudo graeca
Figure 4. The distribution of the differences between the average values for area (A), height (B), and width (C) for the left (LSP) and right (RSP) sides of the plastron relative to straight carapace length (SCL) and the corresponding average value in each SCL class (females n = 79, male n = 76).
Figure 5 in Using digital images in the study of fluctuating asymmetry in the spur-thighed tortoise Testudo graeca
Figure 5. The distribution of the differences between the average values for area (A), height (B), and width (C) for the left (LSP) and right (RSP) sides of the plastron relative to straight carapace length (SCL) and the corresponding average value in each CCL class (females n = 79, male n = 76).
FIGURE 5 in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 5. Processed views of a fossil sample acquired by the optical FPP system OTY. Each view is re-oriented 60º degrees with respect to the prior. Where: "a" indicates a zone that has no information at 60º in this stage of the process and will be corrected with the information of the next view, as is shown in "b".
FIGURE 3 in A novel application of the white light/fringe projection duo: recovering high precision three-dimensional images from fossils for the digital preservation of morphology
FIGURE 3. Magnitude maps of the sample obtained every 60º for 8 and 128 pixels/period. The images show the resolution and detail levels given the number of fringes projected over the sample. The measurements' accuracy of surface and depth depends on the number of projected fringes, which include as many as the system can display (8 pixels/period for each fringe in this case). When the acquisition of details is difficult, a wider fringe is required (based on our sample size, we used 128 pixels).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.