Macros programs ImageJ/FiJi
General use of macros in ImageJ / FiJi : download the file .txt or .ijm to your local plugins folder (Fiji.app\Plugins or ImageJ\Plugins) and restart ImageJ. The corresponding macro will appear in the Plugin menu. To use it : you just need to select it in the menu To modify it : open the file with ImageJ.
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Wound Healing assay surface measurements : 2 Macros
Keywords : biology : Wound healing, confluence, cell migration / image analysis : surface measurement, kinetics, 2D
Input : Phase contrast images
Goal : Measure the migration of the cells through a surface decrease during the closing of the cellular mat
Author : F. Brau / IPMC
Date : June 2013
Input format : .TIFF or .STK image stack openned on ImageJ
What does Surface_blessure.ijm do?
In Images
In a few words
A light intensity correction is applied to the image stack, followed by an edge detection (Find edge), then a low pass filtering to blur the cellular mat. Segmentation of the empty space is done by manual thresholding based on stack histogram. This surface is then measured with the exclusion of small objects : Min area measured at t+1 = 0.01*Min area measured at t. A S=f(t) plot and bi-exponential fit (speed can be calculated ) is given to the user with an image displaying the analsyis done (Blue : surface thresholded / Red : surface measured / Green : original)
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Date : 2018
Input format : Images from Incucyte system stored in a folder
What does Wound_healingv05 do?
In a few words
An unsharp mask followed by a minimum filtering is applied to the image stack. Segmentation is done by a fixed threshold and a filtering on a size criterion to give the surfaces through measurements of ROIs obtained. This macro analyses automatically all the images stored in a folder.
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Adipocyte surface measurements
Keywords : biology : Adipocytes, cell size / image analysis : surface measurement, 2D
Input : A Folder containing RGB images from brightfield microscopy acquisition of adipocyte tissue sections
Goal : Measure the surface of all the adipocytes in the image
Author : F. Brau / IPMC
Date : July 2019
Input format : .TIFF or .JPG 2D image
What does Adipocytes_analyse.ijm do?
In Images
In a few words
The RGB image is splitted, A variance filter is applied on the green image. The cells are segmented by semi-manual percentile thresholding. The areas of all the cells are analysed with the possibility to filter on the size and circularity at the beginning of the analysis. All the TIF or JPEG images of a folder are analyzed. All the results (the image of the "Results", a table with area values, and the distribution) are saved in an automatic sub-folder \Adipocytes_measurements
Used in
Leboucher A, Pisani DF, Martinez-Gili L, Chilloux J, Bermudez-Martin P, Van Dijck A, Ganief T, Macek B, Becker JAJ, Le Merrer J, Kooy RF, Amri EZ, Khandjian EW, Dumas ME, Davidovic L. The translational regulator FMRP controls lipid and glucose metabolism in mice and humans. Mol Metab. 2019 Mar;21:22-35.
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Keywords : biology : Neurons, morphology, dendritic spines/ image analysis : automatic image average
Input : A Folder containing images or stack of images from confocal microscopy acquisition
Goal : Do over contrasted images for morphology analysis from two set of images with two gain and laser adjustements
Macro to be dowloaded : HDR_lsm
Author : F. Brau / IPMC
Date : February 2021
Input format : .lsm or .czi image or stack of images
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- Obj.MPP (Object detection using a Marked Point Process (in Python 3)): Documentation available on a dedicated page.
Available at: http://gitlab.inria.fr/edebreuv/Obj.MPP
- SMLM-CEL0 (Single Molecule Localization in Microscopy – Continuous Exact L0): Single molecule localization in microscopy based on a deconvolution algorithm with a L0-regularization term to promote sparsity. The continuous exact L0 (CEL0) functional is minimized using an iteratively reweighted L1 method (IRL1). This software has been tested within the SMLMS 2016 software benchmarking.
Available at: http://github.com/esoubies/SMLM-CEL0
- SPADE (Small Particle Detector): Python software for detecting a collection of small particles in an image. It is based on a marked point process modeling where the objects belong to a predefined dictionary of shapes. Originally, it has been developed within the ANR project RNAGRIMP for detecting granules in cell cytoplasms.
Available at: http://pypi.python.org/pypi/small-particle-detection and http://gitlab.inria.fr/ncedilni/spade.
- timagetk (Tissue Image Toolkit): Python package dedicated to image processing of multicellular architectures such as plants or animals, and intended for biologists, modelers and computer scientists. Morpheme has contributed to its development by providing a number of image processing tools.
Available at: http://timagetk.readthedocs.io
