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145 results for “Image processing”
A small body open-source dataset for image processing algorithms
<p>Crater-analog dataset acquired with a drone setup at the RIC-DFKI center. The dataset can be used to bridge the domain gap for image processing applications for lunar and small-body missions. </p>
Figure 2. Overall process of the system -An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>This paper mainly focuses on automated detection of White Matter Lesions of brain using<br> fast and efficient clustering algorithms. The goal of clustering a medical image is to simplify the<br> representation of an image into a meaningful image and makes it easier to analyze. As a first step,<br> MRI brain image is pre-processed using Contrast Stretching technique which is one of the efficient<br> image enhancement techniques. The pre-processed image is subjected to clustering. The clustering<br> algorithms include Fuzzy c-means Clustering (FCM), Geostatistical Possibilistic Clustering (GPC)<br> and Geostatistical Fuzzy Clustering Model (GFCM). However clustering techniques are sensitive to<br> initialization and are easily trapped in local optima. In order to obtain an optimized result, the<br> clustered images are undergone optimization. Particle swarm optimization (PSO) is a stochastic<br> global optimization tool which is used in many optimization problems. Figure 2 represents overall<br> process of automatic detection of WMLs of brain. Since MS lesions present different characteristics<br> from lesions in elderly individuals there are many clustering models to determine the accuracy but<br> those methods are not directly applicable to predict the accurate lesions because of the decreased<br> contrast between White Matter and Grey Matter in elderly people. The proposed clustering models<br> are derived by extending the objective functions of FCM and Possibilistic clustering with a<br> Geostatistical (spatial) model. These algorithms are applied to real magnetic resonance images and<br> is shown to be more robust to noise and other artifacts than competing approaches.</p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </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>
Processed airborne hyperspectral images and mosaics of a Paranapanema River region in Capivara reservoir, Brazil
<p>This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm): 505.37, (9.51), 515.31 (14.05), 528.55 (14.82), 539.87 (14.03), 546.99 (14.31), 554.99 (13.26), 560.22 (12.11), 570.44 (14.31), 579.58 (13.26), 592.57 (16.57), 605.73 (14.98), 620.22 (16.26), 625.92 (15.47), 655.06 (12.55), 665.72 (15.59), 670.03 (15.74), 681.33 (15.89), 695.04 (14.96), 700.46 (15.44), 707.96 (15.32), 715.19 (15.37), 725.37 (14.72), 737.29 (14.98), 749.87 (15.08) and 780.1 (14.72). </p> <p>The external orientation parameters (EOP) are acquired by dual frequency GPS and adjusted with tie points computed by bundle adjustment. We perform individual georeferencing on each individual image.</p> <p>This dataset present processing using nine approaches to mosaicking individual georeferenced images. Three of then are new proposed methods developed by the authors. Details of processing and methodology are described on the oficial paper (currently in review process of journal).</p> <p>The authors thank the Graduate Program in Cartographic Sciences (PPGCC) of the School of Science and Technology (UNESP), campus Presidente Prudente, for allowing the development of this research; the National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for financial assistance dedicated to the project. The authors extend special thanks to the São Paulo Research Foundation (FAPESP) for financial support for the hyperspectral camera (2013/50426-4).</p> <p> </p>
Figs 31–32. Zosterodasys transversus, neotype specimens from life. All specimens are from a field sample processed within 24 in Taxonomic Revision and Neotypification of Zosterodasys transversus (Kahl, 1928), with Description of a Mirror-Image Doublet (Ciliophora, Phyllopharyngea, Synhymeniida)
Figs 31–32. Zosterodasys transversus, neotype specimens from life. All specimens are from a field sample processed within 24 hours of collection. 31 – ventral view of a representative neotype cell with an ingested diatom; 32 – variability of body shape and size. Note that the largest specimen (arrow) is almost twice the size of the smaller ones (arrowheads). D – diatoms, OA – oral apparatus, PB – pharyngeal basket. Scale bars: 50 µm (31) and 100 µm (32).
CHARACTERIZATION OF BREAST LESIONS BY PROCESSING DIGITAL BREAST IMAGES
<p><span>This Rendering to the World Health Organization, women in both developed and developing nations are most likely to develop breast cancer. This illness causes breast cells to grow and multiply out of control. According to research institutes and international organizations, there are various screening methods available based on age, and breast cancer can be cured if detected in time. The Breast Imaging Reporting and Data System (BIRADS) is a standardized system that is commonly used in these techniques to report results and findings. Results are sorted by BIRADS into six categories, numbered 0 through 6. Furthermore, mammography is the most widely utilized screening technique.</span></p> <p><span>This study suggests using mammography data processing to identify breast lesions. Adaptive filters are used for image cropping and contrast enhancement during the pre-processing phase. The pectoral muscle is then segmented using segmentation techniques that consider morphological and area growth factors. The lesion is then divided into sections at the muscle and breast levels using the Discrete Wavelet Transform (DWT), which finds any micro calcifications. Furthermore, to distinguish between dense lesions and other kinds of lesions, an area cultivation approach combined with multiple thresholding techniques is employed. Lastly, the obtained segmentation is used to extract textural and morphological features.</span></p> <p><span>When expert-segmented and automatically segmented images were compared, the Sorensen Decade similarity index was 0.73, indicating the effectiveness of the suggested method. Considering that the lesion area on a mammogram can only be roughly delineated by hand or automatically, this is a promising outcome.</span></p>
Dataset for Medical Image Processing in Python Carpentries lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, originally developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="https://zenodo.org/records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a> <div> </div> </li> <li>Additional data: to be added</li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Vector image of processing techniques for self-healing soft robots
<p>Vector images of processing techniques that can be used to manufacture self-healing soft robots.</p> <p>The file includes different types of additive manufacturing processes (fused filament fabrication, direct ink writing, selective laser sintering, stereolithography, inkjet printing, fused granulate fabrication), formative processes (compression moulding, solvent casting, injection moulding, casting, vacuum assisted resin transfer moulding, blow moulding), and assembly processes (folding & binding, joining & binding, stacking and binding, local thermal ablation & welding).</p>
Processed single cell data from CODEX multiplexed imaging of the human intestine
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on 64 sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006, B008, B009, B010, B011, and B012) using a panel of 57 oligonucleotide-barcoded antibodies. 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. The outputs of this process were data frames of 2.6 million cells with 57 antibody fluorescence values quantified from each marker. Each cell has its cell type, cellular neighborhood, community of neighborhooods, and tissue unit defined with x, y coordinates representing pixel location in the original image. This is from a total of 25 cell types, 20 multicellular neighborhoods, 10 communities of neighborhoods, and 3 tissue segments that could be used to understand the cellular interactions, composition, and structure of the human intestine from the duodenum to the sigmoid colon and understand differences between different areas of the intestine. This data could be used as a healthy baseline to compare other single-cell datasets of the human intestine, particularly multiplexed imaging ones. </p> <p>The overall structure of the datasets is individual cells segmented out in each row. Columns MUC2 through CD161 are the markers used for clustering the cell types. These are the columns that are the values of the antibody staining the target protein within the tissue quantified at the single-cell level. This value is the per cell/area averaged fluorescent intensity that has subsequently been z normalized along each column as described above. OLFM4 through MUC6 were captured in the quantification but not used within the clustering of cell types. Other columns are explained in the table in the Usage Notes section below.</p> <p>Along with this main data table, there is also a donor metadata table that links the donor ids to clinical metadata such as: age, sex, race, BMI, history of diabetes, history of cancer, history of hypertension, and history of gastorintestinal disease.</p> <p>The raw imaging data can be found at (<a href="https://portal.hubmapconsortium.org/">https://portal.hubmapconsortium.org/</a>). We have created a landing page with links to all the raw dataset IDs and the HuBMAP ID for this Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356. This can be used to also pair it with the matched snRNAseq and snATACseq for each section of tissue.</p>
Dataset Literature Review Digital Forensic and Image Processing
<p>Data ini digunakan untuk membuat penelitian sesuai dengan tinjauan literatur dengan kata kunci "<em>digital forensic</em>" dan "<em>image processing</em>"</p>
Dataset Literature Review Digital Forensic AND Image Processing
<p>Data ini digunakan untuk membuat penelitian berdasarkan tinjauan literatur dengan kata kunci "digital forensic" dan "image processing" </p>
Dataset for surface waves height prediction through the video and image processing
<p>Image-based study of surface waves is a long lasting topic in ocean science and remote sensing. We believe that modern computers and new programming techniques can make a break-through in this area.</p> <p> </p> <p>This dataset provides some video files of surface wind waves of two kinds. First is a video snapshot of a quite large area. Second one is a zoom-in video of a spar-buoy (a stick) located in this field. According to the zoom-in video we may see the actual height of the wave in this particular point. This should be treated as a reliable data and so it can be used to calibrate the brightness field. I.e. the users of this dataset are welcome to train their model to obtain the height of the wave out of its brightness on the zoom-out large-area videos.</p> <p> </p> <p>All video files are readable by a conventional software. Records were taken at mild wind conditions in a gulf (fjord or skerry) of the Ladoga Lake. See "readme.pdf" for the details</p>
Processed Vectra images for primary central nervous system lymphoma (PCNSL) patients
<ul> <li>FFPE materials</li> </ul> <p>Formalin-fixed paraffin-embedded (FFPE) tumor samples and clinical data were obtained from PCNSL patients enrolled in the HOVON105/ALLG NHL 24 intergroup, multicenter, open-label, randomized phase 3 study (NTR2437 and ACTRN12610000908033) through the HOVON Pathology Facility and Biobank. </p> <ul> <li>Muiltiplex imaging</li> </ul> <p>Multiplex immunofluorescence was performed on 4-µm-thick formalin-fixed, paraffin-embedded whole tissue sections using the Opal 7-color fluorescence immunohistochemistry (IHC) kit (Akoya biosciences, USA), as previously described. In brief, slides were deparaffinized and rehydrated, followed by a blocking step for endogenous peroxidase using 0.3% H<sub>2</sub>O<sub>2</sub>/methanol and fixation with 10% neutral buffered formalin (Leica Biosystems, Germany). Slides were washed in Milli-Q water and 0.05% Tween20 in 1x Tris-Buffered Saline (TBS-T). Antigen retrieval was done by placing the slides in 0.05% ProClin300/Tris–EDTA buffer pH 9.0 in a microwave at 100% power until boiling, followed by 15 min at 30% power. Slides were cooled in Milli-Q water, washed in 1x TBS-T and blocked with Antibody Diluent (Agilent, USA). The slides were then incubated with primary antibody diluted in Normal Antibody Diluent, followed by incubation with the broad spectrum HRP from the SuperPicture Polymer Detection Kit (Life Technologies, USA). Next, the slides were incubated with Opal TSA fluorochromes diluted in an amplification buffer (Akoya biosciences, USA). The primary and secondary antibody complex was stripped by microwave treatment with 0.05% ProClin300/Tris–EDTA buffer at pH 9.0. Finally, DAPI working solution (Akoya biosciences, USA) was applied and the slides were mounted with Prolong Diamond Anti-fade mounting medium (#P36965; Life Technologies).</p> <ul> <li>Image processing</li> </ul> <p>Stained slides were scanned using the Vectra Polaris Automated Quantitative Pathology Imaging System (Akoya biosciences, USA). From each slide, representative tumor regions and regions at the junction of tumor and surrounding cerebral tissue were selected and multispectral imaging (MSI) images were acquired at 40x resolution. After image capture, the images were spectrally unmixed and analyzed, using supervised machine learning algorithms within Inform 4.2.2. (Akoya biosciences). Cells were assigned into ten different phenotype categories: “PAX5+PD-L1-”, “PAX5+PD-L1+”, “CD163+PD-L1-”, “CD163+PD-L1+”, “CD3+CD8-PD-1-”, “CD3+CD8+PD-1-”, “CD3+CD8-PD-1+”, “CD3+CD8+PD-1+”, “other PD-L1+” or “other”, based on the size of the cells and positivity of markers in the panel. </p> <p> </p>
Human intestine processed CODEX multiplexed images for donors B004-6, B008 (Part 1/2)
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Human intestine processed CODEX multiplexed images for donors B009-B012 (Part 2/2)
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Data for: Image processing tools for petabyte-scale light sheet microscopy data (Part 2/2)
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Data for: Image processing tools for petabyte-scale light sheet microscopy data (Part 1/2)
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Microscopic images and schematics illustrating processes of microenvironment sensing and cortical actomyosin partitioning in T cells
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Processed single cell data from CODEX multiplexed imaging of the human intestine
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ScienceDex guides
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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.