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Imagej

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ImageJ is an open-source image processing software designed for scientific use. Its core function is to provide users with a versatile platform for viewing, analyzing, and processing digital images. ImageJ supports a wide range of image file formats and offers a variety of tools for image manipulation, measurement, and analysis.

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Lab products found in correlation

Matlab, by MathWorks (43 mentions) Fluo 4 am, by Thermo Fisher Scientific (2 mentions) Matlab 7, by MathWorks (2 mentions) Ix81 inverted fluorescence confocal microscope, by Olympus (2 mentions) Orca er digital camera, by Hamamatsu Photonics (2 mentions) Prism 6, by GraphPad (1 mentions) Metamorph, by Universal Imaging (1 mentions) Imagej, by Universal Imaging (1 mentions) Illustrator cs6, by Adobe (1 mentions) Matlab 2016a, by MathWorks (1 mentions) Ix81 microscope, by Olympus (1 mentions) Orca flash4.0 v3, by Hamamatsu Photonics (1 mentions) Metamorph software, by Molecular Devices (1 mentions) Axiovert lsm 880, by Zeiss (1 mentions) Axiovert lsm 880 confocal microscope, by Zeiss (1 mentions) Matlab software, by MathWorks (1 mentions) Imaris software, by Oxford Instruments (1 mentions) Eclipse te2000 e, by Nikon (1 mentions) Labview, by Texas Instruments (1 mentions) Cfi plan apo λ, by Nikon (1 mentions)

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ImageJ 1.52p (2 citations) ImageJ/Fiji (2 citations)

47 protocols using imagej

1

Quantitative Image Analysis of Cells

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Analysis of cells and clusters during time-lapse experiments was performed using ImageJ (public domain) and a custom MATLAB script (MathWorks; Natick, MA). Analysis of vessel characteristics was performed using ImageJ (public domain) and MetaMorph (Universal Imaging, Downingtown, PA). Analysis of stress fibers was performed using ImageJ (public domain) and a custom MATLAB script developed in the laboratory (MathWorks; Natick, MA) (60 (link)). Analysis of GFP-ECFCs in vivo was performed using Imaris (Bitplane, Belfast, UK) and ImageJ (public domain). All graphs were made using GraphPad Prism 6 (GraphPad Software Inc.).
Blatchley M.R., Hall F., Wang S., Pruitt H.C, & Gerecht S. (2019). Hypoxia and matrix viscoelasticity sequentially regulate endothelial progenitor cluster-based vasculogenesis. Science Advances, 5(3), eaau7518.
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2

Multidisciplinary Data Analysis Pipeline

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Data analysis was performed in Python, ImageJ, and Matlab (The
Mathworks).
Smith G.B., Hein B., Whitney D.E., Fitzpatrick D, & Kaschube M. (2018). Distributed network interactions and their emergence in developing neocortex. Nature neuroscience, 21(11), 1600-1608.
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3

Image Analysis of Biofilms and Cells

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ImageJ (National Institutes of Health, ImageJ.nih.gov/ij/">http://ImageJ.nih.gov/ij/) and MATLAB (MathWorks) were used for image processing and analysis. The detailed image analysis methods for biofilms and single cells are described below respectively.
Zhang F., Kwan A., Xu A, & Süel G.M. (2015). A Synthetic Quorum Sensing System Reveals a Potential Private Benefit for Public Good Production in a Biofilm. PLoS ONE, 10(7), e0132948.
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4

Automated Colony Analysis using Lensless Images

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The image analysis described below was performed by using ImageJ [25 (link)] and MATLAB (The MathWorks, Inc., Massachusetts, USA). First, the contrast of original lensless images was enhanced by remapping the data values to fill the entire intensity range of (0, 255) using the auto-adjusting function for intensity values in MATLAB. Subsequently, each pixel value is subtracted from the maximum pixel value, and the difference is used as the pixel value in the output images; i.e., black-and-white balance is inverted. Then, the images were binarized with Otsu’s thresholding method [26 ] by which the pixels were represented in 10 gray levels, and dichotomized into two classes (i.e., background and colony regions) with a threshold at the maximum level. After the binarization, fill-up processing was executed to determine colony regions. Finally, discriminative parameters (histogram deviation, G; donutness, D; entropy, H; and energy density, E), which are described in detail below, were calculated from the contrast-adjusted and inverted lensless images at the colony regions.
Maeda Y., Dobashi H., Sugiyama Y., Saeki T., Lim T.K., Harada M., Matsunaga T., Yoshino T, & Tanaka T. (2017). Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies. PLoS ONE, 12(4), e0174723.
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5

Quantitative Image Analysis Methods

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Fluorescence and brightfield images were acquired as uncompressed bitmaps in BMP or TIFF format, and figures were assembled in Adobe Illustrator CS6. Quantitative analysis of calcium signaling, tissue contractions, movements of cell nuclei, changes in cell geometry, cell expulsions and actin dynamics was carried out in ImageJ (http://rsbweb.nih.gov/ij) and MATLAB (Mathworks). See Supplemental Experimental Procedures for details.
Herrgen L., Voss O.P, & Akerman C.J. (2014). Calcium-dependent neuroepithelial contractions expel damaged cells from the developing brain. Developmental cell, 31(5), 599-613.
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6

Tracking Neutrophil Migration in Airway Model

Cited 3 times
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Neutrophil migration across an infected airway was studied using an inverted air-liquid interface (ALI) in vitro co-culture model and protocol as previously reported (19 (link), 20 (link)). Paired experiments (n=7) were performed sequentially with CF-outpatient and non-CF neutrophils, with variation in the order of analysis from experiment to experiment. Sub-cellular resolution, 3D time-lapse µOCT (18 (link), 19 (link)) videos of the neutrophil migration were recorded by scanning the µOCT imaging beam over a 1×1 mm lateral width every 10 minutes for 2 hours. Using binarized µOCT images, volumes representing neutrophils in the apical compartment were quantified over time to study the kinetics of neutrophil transepithelial migration. The volume of neutrophil migration was converted to numerical counts based on the pre-determined mean diameter of neutrophils, (Supplemental Fig. 1a; 13.5 ± 2.2 µm, mean ± S.D.), which was similar between the CF-outpatient and non-CF groups. The mean neutrophil column area, representing a cluster of neutrophils migrating across the epithelium in unison, was determined from en face images at approximately 10 µm below the monolayers. Adherent neutrophil columns in binarized en face images were segmented with Watershed algorithm. Image processing and calculations were performed using ImageJ (30 (link)) and Matlab (Mathworks Inc).
Yonker L.M., Marand A., Muldur S., Hopke A., Min Leung H., De La Flor D., Park G., Pinsky H., Guthrie L.B., Tearney G.J., Irimia D, & Hurley B.P. (2021). Neutrophil Dysfunction in Cystic Fibrosis. Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society, 20(6), 1062-1071.
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7

Automated Image Analysis for Microbiological Assays

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The image analysis described below was performed using ImageJ [24 (link)] and MATLAB (The MathWorks, Inc., Natick, Massachusetts, MA, USA). First, the contrast of original lens-less images was enhanced by remapping the data values to fill the entire intensity range (0, 255) using the auto-adjusting function for intensity values in MATLAB. Subsequently, each pixel value is subtracted from the maximal pixel value, and the difference is used as the pixel value in the output images; i.e., black-and-white balance is inverted. Then, the images were binarized based on the Otsu’s thresholding method [25 (link)], by which the pixels were represented in 10 gray levels, and dichotomized into two classes (i.e., background and colony regions) with a threshold at the maximum level. After the binarization, fill-up processing was executed to determine colony regions. Finally, discriminative parameters, which are described in detail below, were calculated from the contrast-adjusted and inverted lens-less images.
Maeda Y., Sugiyama Y., Kogiso A., Lim T.K., Harada M., Yoshino T., Matsunaga T, & Tanaka T. (2018). Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches. Sensors (Basel, Switzerland), 18(9), 2789.
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8

Automated Measurement of p-ERK and p-p38

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p-ERK and p-p38 levels were automatically measured in nuclei with homemade programs in MATLAB (MathWorks) and ImageJ (http://rsb.info.nih.gov/ij/).
Senecal A., Munsky B., Proux F., Ly N., Braye F.E., Zimmer C., Mueller F, & Darzacq X. (2014). Transcription Factors Modulate c-Fos Transcriptional Bursts. Cell reports, 8(1), 75-83.
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9

In Vivo Two-Photon Calcium Imaging

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In vivo two-photon calcium imaging was performed on a Prairie Ultima Multiphoton microscope equipped with a mode-locked Ti:Sapphire laser (Coherent Chameleon Ultra II, Coherent, Santa Clara, CA, USA) and the power of excitation on the sample was <15 mW. Acquisitions were performed with spiral scanning at about 4 Hz with a water immersion lens (Olympus, 20×, 1.00 NA, Tokyo, JP) at a resolution of 512 × 512 pixels at an excitation of 920 nm. The imaging field was circular with a diameter of 607 µm. Imaging data were analyzed with ImageJ and custom Matlab code (Mathworks, Natick, MA, USA).
The motion of the fish is calculated from the time-lapse sequences as the difference between the position of the larva at each time point, computed as the area occupied by the fish and the median position of the larva itself, calculated over the entire time-lapse sequence.
Cozzolino O., Sicca F., Paoli E., Trovato F., Santorelli F.M., Ratto G.M, & Marchese M. (2020). Evolution of Epileptiform Activity in Zebrafish by Statistical-Based Integration of Electrophysiology and 2-Photon Ca2+ Imaging. Cells, 9(3), 769.
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10

Computer-Assisted Myelination Quantification

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Myelination in the oligodendrocyte-neuron co-culture was semi-automatically quantified using the Computer-assisted Evaluation of Myelin formation (CEM) tool [25 (link)]. Myelination was assessed by analyzing individual oligodendrocytes. Upon lentiviral transduction, Gfp/Mbp-positive oligodendrocytes were randomly selected from at least 8 different wells per experiment and condition by a blinded researcher. Images were processed using ImageJ (generation of binary images; calculation of Mbp-positive, Tuj1-positive, and Mbp/Tuj1-positive pixels; pictures with less than 200,000 Tuj1 pixels were excluded) and MATLAB (MathWorks; cell body removal). Myelin pixels (Mbp/Tuj1-positive) relative to total Mbp-positive pixels were calculated and considered as myelination (in percentage) of an individual oligodendrocyte.
Ettle B., Kerman B.E., Valera E., Gillmann C., Schlachetzki J.C., Reiprich S., Büttner C., Ekici A.B., Reis A., Wegner M., Bäuerle T., Riemenschneider M.J., Masliah E., Gage F.H, & Winkler J. (2016). α-Synuclein-induced myelination deficit defines a novel interventional target for multiple system atrophy. Acta neuropathologica, 132(1), 59-75.
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