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145 results for “Image processing”
The Inflammatory Process and the Medical Imaging in Patients With an Inflammatory Disease of the Central Nervous System.
ClinicalTrials.gov study NCT01567553. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Artificial Intelligence-based Image Processing Methods to Advance the Characterization of Polycystic Kidney Disease
ClinicalTrials.gov study NCT06688981. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
ASPIDE: SERMAS diffusion-weighted image processing use-case
<p>This repository contains the outputs of the diffusion-weighted image processing use-case of the ASPIDE project for a single subject. The input files were obtained from the Human Connectome Project (HCP) database. HCP is an open project and the full unprocessed dataset can be obtained from <a href="https://db.humanconnectome.org/app/template/Login.vm">https://db.humanconnectome.org/app/template/Login.vm</a>.</p>
Data from: Auditory functional magnetic resonance imaging in dogs – normalization and group analysis and the processing of pitch in the canine auditory pathways
Background: Functional magnetic resonance imaging (fMRI) is an advanced and frequently used technique for studying brain functions in humans and increasingly so in animals. A key element of analyzing fMRI data is group analysis, for which valid spatial normalization is a prerequisite. In the current study we applied normalization and group analysis to a dataset from an auditory functional MRI experiment in anesthetized beagles. The stimulation paradigm used in the experiment was composed of simple Gaussian noise and regular interval sounds (RIS), which included a periodicity pitch as an additional sound feature. The results from the performed group analysis were compared with those from single animal analysis. In addition to this, the data were examined for brain regions showing an increased activation associated with the perception of pitch. Results: With the group analysis, significant activations matching the position of the right superior olivary nucleus, lateral lemniscus and internal capsule were identified, which could not be detected in the single animal analysis. In addition, a large cluster of activated voxels in the auditory cortex was found. The contrast of the RIS condition (including pitch) with Gaussian noise (no pitch) showed a significant effect in a region matching the location of the left medial geniculate nucleus. Conclusion: By using group analysis additional activated areas along the canine auditory pathways could be identified in comparison to single animal analysis. It was possible to demonstrate a pitch-specific effect, indicating that group analysis is a suitable method for improving the results of auditory fMRI studies in dogs and extending our knowledge of canine neuroanatomy.
Processed CODEX multiplexed imaging data of cellular microenvironment around T cell stimulating hydrogels
<p>Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into the cellular microenvironment surrounding T cell stimulating hydrogels. These hydrogels were engineered with signals that could locally expand antigen-specific T cells for use in tumor immunotherapy. CODEX imaging involves an iterative process of annealing and stripping fluorophore-labeled oligonucleotide barcodes, complementing the barcodes attached to over 40 antibodies used for tissue staining. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue.</p> <p>Our datasets comprise individual cells as rows, each characterized by 40+ antibody fluorescence values quantified from various markers evaluated for each study. These markers correspond to the antibodies targeting specific proteins within the tissue, quantified at the single-cell level. The values represent per-cell/area-averaged fluorescent intensities, z-normalized along each column. Each cell is mapped with its cell type, defined by x and y coordinates representing pixel locations in the original image. </p> <p>We then used this data to investigate how different proportions of the cell types change over time in response to the stimulating hydrogel injection with antigen-specific T cells. These data could be used to understand the cellular interactions, composition, and structure of T cell stimulating biomaterials for antigen-specific immunotherapy and with adoptive T cell transfer. These datasets offer valuable insights for researchers interested in engineering T cell stimulating microenvironments, immune responses, and therapeutic interventions such as T cell therapies.</p> <p>We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and hydrogel environment in a murine melanoma model. We injected antigen-specific T cells with microparticle T cell stimulating hydrogels into mice subcutaneously. Injection sites were take out at different time points day=0 (just after injection), day=3, and day=9 (n=3-6 per time point). Our 51-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, and stromal cell types, resulting in a rich dataset of 241,685 cells across 51 marker channels.</p>
Coastal Wetland Crab Image Dataset and Image Processing Model Weights
<p>This data was collected from the distribution range of mangroves along the coast of China and has been randomly selected and manually corrected to be labeled as a crab image analysis dataset. The weights of deep learning models trained on YOLOv5/v8 and EfficientNet are also uploaded simultaneously. Additionally, it includes the necessary test datasets and some test results. Please cite when using this dataset, and contact the administrator if you need help. The specific directories are as follows:</p> <p>- R-crab: Contains code needed to test model performance, with the subfolder data containing the test dataset required. It includes (1) crab-man as human-marked references, crab-ref as model detection results. (2) luoyuan-burrow for the detection results of crab burrows in the Luoyuan area case study, and luoyuan-crab for crab detection results, including crab classification, localization, and carapace width information. (3) method-test for testing different methods, i.e., whether to use a two-stage detection model. (4) size-conf-test records the model detection results under different image input sizes and confidence threshold levels. (1), (3), and (4) are completed in Out-of-sample data, while (2) is completed in Luoyuan. fig_attr.csv records the test results of image attributes on detection accuracy. label_results.csv records the comparison results between traits measured manually using ImageJ software and our designed model for detecting crab carapace width. luoyuan_list.csv records the numbering information of Luoyuan sampling plots.<br>- Aiweights: Contains model weights trained based on Object detection data, with cpm-model under v8n-seg-crab.pt for YOLOv8 trained to extract crab carapace width. Sfc-model under adam20.pth is a two-stage detection model trained based on EfficientNet. Trained_weights under burrow-baseline is a crab burrow detection model trained based on our improved YOLOv5 (improvements stored at https://github.com/GuuX29/crab-yolo-add, same below); frame-s6-2560.pt is a plot frame detection model for obtaining standard 50*50cm plot images; s-simam-20.pt and x-simam-20.pt are both for crab detection and classification models, with x having higher accuracy.<br>- Luoyuan: crop stores standardized processed Luoyuan image data, numbered as above, test stores detection results, same as R-crab.<br>- Object detection: Contains cropped 640 pixels crab field sampling images, with images in the images folder and bounding box labels in the labels folder, divided into training and testing at a 9:1 ratio, train.txt and val.txt record the allocation information.<br>- Out-of-sample: Records 100 independent test images not used to train the model, stored in images, crab-man, and crab-ref are the same as in R-crab.<br>- Segmentation: Stores image data used to test model detection of crab carapace width, with results also stored in R-crab.</p> <p>We hope this data and method will benefit the progress of research in this field. For any suggestions for improvement and help with usage, please contact the administrator. guuxuan1994@gmail.com </p>
Dataset - Ghost Image Processing
<p>Dataset to accompany the paper titled: Ghost Image Processing. (to be submitted)</p> <p>Files in .txt format with '_ReadMe.txt' providing context. </p>
MATLAB code for processing images of the in-situ etching process and analysis of etching kinetics for MAX phase to MXene transformation
Open the record for dataset details and reuse information.
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>This is an open dataset.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of <i>O</i>(<i>n</i><sup>3</sup>) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.</p>
Figure 2 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 2 - Virtual configurations used for software validation. Virtual configurations used to compile the data presented in the Table 1. Part 2.8 is one of the 10 replicates obtained with a random distribution. All other configurations have been designed in order to reach the desired level of aggregation and affinity between groups. The filled and empty shapes represented two virtual groups in the population.
Figure 1 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 1 - Flow chart of the creation of a new NEIGHBOUR-IN file. This figure presents the different steps in the creation of a new file, from the importation of the snapshot to the calculation of the statistics of dispersion.
Figure 4 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 4 - Spatial distribution in woodlice. Graphic outputs of spatial distribution patterns obtained in three configurations with monospecific or bispecific populations including two groups of eight individuals: a PD-PD: The two groups are Porcellio dilatatus (red and green) b PD-PS: Porcellio dilatatus (red) and Porcellio scaber (green) c PD-AV: Porcellio dilatatus (red) and Armadillidium vulgare (green). The outputs show 64 cells. Each cell is represented with a colour corresponding to the individual(s) in that cell. The colour is mixed using green and red proportional to the number of green and red individuals. If the cell is empty, the colour is black. The intensity of the colour reflects the number of individuals. The position of the individual is determined by its point G (centre-point).
Figure 3 from: Caubet Y, Richard F-J (2015) NEIGHBOUR-IN: Image processing software for spatial analysis of animal grouping. In: Taiti S, Hornung E, Štrus J, Bouchon D (Eds) Trends in Terrestrial Isopod Biology. ZooKeys 515: 173–189. https://doi.org/10.3897/zookeys.515.9390
Figure 3 - Aggregation heterogeneity in woodlice. Aggregation patterns of two groups of woodlice illustrating the Aggregation Heterogenity Index (AHI) and the Spatial Mixed Index (SMI). PD: Porcellio dilatatus, PS: Porcellio scaber, CC: Cylisticus convexus. Values of indexes: PD-PD: AHI=0.93 & SMI=0.80; PD-PS: AHI=0.67 & SMI=0.60; PD-CC: AHI=0.63 & SMI=0.33.
Microhabitat selection of meadow and steppe vipers enlightened by digital photography and image processing to describe grassland vegetation structure
<p>Dataset</p> <ol> <li> <p>Understanding animals’ selection of microhabitats is important in both ecology and biodiversity conservation. However, there is no generally accepted methodology for the characterisation of microhabitats, especially for vegetation structure.</p> </li> <li> <p>We studied microhabitat selection of <em>Vipera</em> snakes by comparing grassland vegetation structure between viper occurrence points and random points in three grassland ecosystems: <em>V. graeca</em> in mountain meadows of Albania, <em>V. renardi</em> in loess steppes of Ukraine, and <em>V. ursinii</em> in sand grasslands in Hungary. We quantified vegetation structure in an objective manner by automated processing of images taken of the vegetation against a vegetation profile board under standardised conditions. We developed an R script for automatic calculation of four vegetation structure variables derived from raster data obtained in the images: leaf area (LA), height of closed vegetation (HCV), maximum height of vegetation (MHC), and foliage height diversity (FHD).</p> </li> <li> <p>Generalized linear mixed models revealed that snake occurrence was positively related to HCV in <em>V. graeca</em>, to LA in <em>V. renardi</em> and to LA and MHC in <em>V. ursinii</em>, and negatively to to HCV in <em>V. ursinii</em>.</p> </li> <li> <p>Our results demonstrate that vegetation structure variables derived from automated image processing significantly influence viper microhabitat selection. Our method minimises the risk of subjectivity in measuring vegetation structure, allows upscaling if neighbouring pixels are combined, and is suitable for comparison of or extrapolation across different grasslands, vegetation types or ecosystems.</p> </li> </ol>
Unedited photographs of Mycobacterium tuberculosis colonies to practice an image processing protocol
<p>Unedited raw photographs in TIFF file format of <em>Mycobacterium tuberculosis </em>(Mtb) colonies grown in 7H10 agar plates inside a BSL-3 facility. To assay the bactericidal effect of different drug treatments, serial dilutions were performed per treatment in plate quadrants to ensure a countable range of colony-forming units (CFU). Each treatment has three replicates and a control without the antibacterial treatment. The plates were incubated until colonies grew to about 1–2 mm in size. Photographed using a Sony Alpha 7R II full-frame mirrorless color camera (7974 x 5316 pixels) under ambient laboratory lighting at a distance of 12–18 inches from the plates after placing the plates on a black background and behind the glass of the biosafety cabinet.</p>
Reversible image enhancement processing via hybrid quantum algorithms
<p> In order to solve the problems of pixel distortion, image clarity, and detail reduction during reversible image enhancement processing, a reversible image enhancement method with a hybrid quantum algorithm is proposed. Based on classical image processing technology, the algorithm first extracts the feature information of the original image and converts the classical image into a quantum image form by qubit encoding. Then the image enhancement is initially realized by Quantum Fourier transform. Finally, in order to avoid the loss and destruction of information, the Quantum Fourier inverse transform is used to reverse the processing of the image. Compared with the traditional image enhancement method, this method has better reversibility and fidelity and can take advantage of quantum parallel computing to improve image processing efficiency.</p>
A Study of Flurpiridaz (18F) Injection for PET Imaging for Assessment of MPI Quality Using HPLC and SPE Manufacturing Processes
ClinicalTrials.gov study NCT04594941. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Half-Dose Molecular Breast Imaging With Wide Beam Reconstruction Processing
ClinicalTrials.gov study NCT01653964. IPD Sharing: NO. Countries: 1. Publications: 0.
Magnetic Resonance Imaging Studies of Motor and Thought Processes
ClinicalTrials.gov study NCT00001361. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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.