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368 results for “imaging system”
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-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 4. Mouse trajectory – images taken from http://iographica.com 4 hours in Photoshop (left) vs. 4 hours in Eclipse (right)
<p>Studies from different fields (Arroyo & Wei, 2006), (Mäkiaho & Poranen, 2012), (Lockton, Harrison, Cain, Stanton, & Jennings, 2013), (Seelye, et al., 2015), (Hehman, Stolier, & Freeman, 2014) have shown that mouse trajectory and clicks are environment (application, device) and user specific, especially if mapped over a long working session (Figure 4). </p>
Data and results for manuscript: "Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurements"
<p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript titled:<br> <br> Imaging and functional characterization of crop root systems using spectroscopic electrical impedance measurement</p>
Data, analysis scripts, simulations, and schematics files for "Position Sensitive Alpha Detector for an Associate Particle Imaging System"
<p>Measured data and analysis script, as well as, simulated data, input scripts, as well as, schematics for the readout board for our publication "Position Sensitive Alpha Detector for an Associate Particle Imaging System"</p>
Example structure of data sent from a collection management system to a citizen science platform, multi-imaged case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Multi-imaged vertebrate specimen case : http://coldb.mnhn.fr/catalognumber/mnhn/zo/2013-152</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
FIGURE 11 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 11. Fossils (bones and teeth) and a mixture of fossils and sediment (sand and gravel) from Sample 3. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image (grayscale) under UV light. 4, Black and white image after segmentation of the blue channel image.
FIGURE 9. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 9. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 3.
FIGURE 10 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 10. Examples of spectral images of Sample 3 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.
FIGURE 4 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 4. Examples of spectral images of Sample 1 through spectral bands (420, 520, 620, 670, and 720 nm) taken under daylight. An RGB image computed using the sRGB - standard RGB colour space is also provided.
FIGURE 8 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 8. RGB images of Sample 2 with ferrous particles, bones-teeth, sand-gravel and a mixture of bonesteeth and sand-gravel. 1, Under daylight (D65). 2, Under UV Light.
FIGURE 3. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 3. 1, Reflectance spectra of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1. 2, Fluorescence spectra (radiance in W/sr·cm2) of sediment (sand and gravel) and fossils (bones and teeth) of Sample 1.
FIGURE 6. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 6. 1, Reflectance spectra of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2. 2, Fluorescence spectra (radiance in W/sr*cm2) of sediment (sand and gravel), fossils (bones and teeth) and ferrous sediment of Sample 2.
FIGURE 1. 1 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 1. 1, Wet sieving process of palaeontological samples using Freudenthal's technique. 2, Visual recognition and separation of teeth and bones using a binocular microscope and pincers.
FIGURE 7 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 7. Examples of spectral images of Sample 2 through spectral bands (420, 520, 620, 670 and 720 nm) taken under daylight. An RGB computed using the sRGB - standard RGB colour space image is also provided.
FIGURE 5 in Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study
FIGURE 5. Mixture of bones-teeth and sand-gravel from Sample 1. 1, RGB image under daylight (D65). 2, RGB image under UV light. 3, Blue channel image under UV light. 4, Green channel image (grayscale) under UV light. 5, Black and white image after segmentation of the green channel image.
Examples of ancient Near Eastern artifacts imaged and visualized with PLD system
<p><strong>From top to bottom: a coin, a cylinder seal impression, impressions on the bottom of a funerary cone, and a cuneiform tablet. For each artifact four visualizations were generated with the PLD MLR viewer. From left to right: coloor, shaded, automated sketch, normal map. References for the objects: Greek silver coin: o.i. 522 (©️ KU Leuven Art Collection); Modern impression Old Akkadian cylinder seal: O.861 (©️ Art & History Museum, Brussels - RMAH); Old Egyptian funerary cone: E.3984 (©️ Art & History Museum, Brussels); Old Akkadian cuneiform tablet: O.95 (©️ Art & History Museum, Brussels).</strong></p>
A high-throughput multispectral imaging system for museum specimens
<p>We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens.</p>
Data associated to the manuscript "Direct Imaging of Micrometer Thick Interfaces in Salt-Salt Aqueous Biphasic Systems"
<p>Supporting data for the article:</p> <p>Direct Imaging of Micrometer Thick Interfaces in Salt-Salt Aqueous Biphasic Systems</p> <p>PNAS 2023, doi: 0.1073/pnas.2220662120</p> <p>The folder contains binodal curves and surface tension measurements, FTIR and NMR raw data, and Raman imaging data.</p> <p> </p>
Image dataset for the creation of an automatic system for meteor fall detection
<p>Image dataset with sky photos showing the occurrence or non-occurrence of falling meteors. The database comprises 7,000 images in JPEG format -- 3,850 (55%) images show the event of falling meteors, and 3,150 (45%) images show no meteors. Different instruments captured the photos from 2014 to 2023. We used the images to train a deep-learning neural network for an automatic falling meteor detector.</p> <p>The primary image data sources were the Brazilian Meteor Observation Network (BRAMON -- <a href="http://www.bramonmeteor.org">http://www.bramonmeteor.org</a>), UK Meteor Network (UKMON -- <a href="https://ukmeteornetwork.co.uk">https://ukmeteornetwork.co.uk</a>), and <em>Base des Observateurs Amateurs de Météores</em> (BOAM -- <a href="http://boam.fr">http://boam.fr</a>) repositories.</p> <p><strong>Folders Structure</strong></p> <p>We divided the folder structure into two levels. In the first level, we have two folders: RawImages, which holds images with captions stored in the repositories; and CroppedImages, which contains images without the captions (we cropped a band of 24 pixels in the lower part of the image).</p> <p>In the second level, in each of the previous folders, we have another two folders: meteor, which has images with meteors; and non-meteors, with images without occurrences of meteors.</p> <p><strong>Naming pattern for the files</strong></p> <p>The naming pattern in the meteor folder follows the format <source>_<date>_<id>.jpg where:</p> <ul> <li><source> is one of the 3 data sources: bramon, ukmon, or boam.</li> <li><date> is the date-time the instrument captured the image in the format yyyymmdd_hhnnss (y:year, m:month, d:day, h:hours, n:minutes, s:seconds).</li> <li><id> is an identifier from a specific source to avoid date-time conflicts: <ul> <li>BRAMON: radiant identifier.</li> <li>UKMON: station identifier.</li> <li>BOAM: station identifier.</li> </ul> </li> </ul> <p>For the non-meteor folder, the naming pattern is <source>_<date>_nonmeteor.jpg to avoid homonyms (with the same date-time) and to identify that they are images of non-meteors.</p>
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.