PLEASE NOTE!This is an older version tree of MIB, the newer version for Matlab R2014b and newer available from herehttp://se.mathworks.com/matlabcentral/fileexchange/63402-microscopy-image-browser-2--mib2-With MIB you can analyse, segment and visualize various multidimensional datasets from both light and electron microscopy. See more further details and tutorials on MIB website: http://mib.helsinki.fi/index.htmlI would like to acknowledge Matlab File Exchange user community and especially the authors whose functions were utilized during development of the program:http://mib.helsinki.fi/acknowledgements.html
With MIB2 you can analyse, segment and visualize various multidimensional datasets from both light and electron microscopy. MIB2 is completely rewritten to follow MVC architecture and brings additional stability among many new features. See more further details and tutorials on MIB website: http://mib.helsinki.fiI would like to acknowledge Matlab File Exchange user community and especially the authors whose functions were utilized during development of the program: http://mib.helsinki.fi/acknowledgements.htmlThe MIB version 1 is available from here http://se.mathworks.com/matlabcentral/fileexchange/56481-microscopy-image-browser--mib- and recommended for Matlab version: R2011a - 2014aList of all features with video tutorials is available from http://mib.helsinki.fi/features_all.htmlFeatures:Support 2D-4D datasets (x,y,c,z,t) Up to 9 simultaneously opened datasets Bounding box for each dataset Extendible via plugins Log of performed actions Customizable undo system Customizable keyboard shortcuts Colorblind friendly color schemes Regions of interestsVirtual stacking mode for working with datasets that are larger than available memory Batch processing mode Data import/exportDirect import/export with Matlab , Fiji , Imaris and system clipboard Direct import from Omero server and URL links Load and save to TIF, Amira Mesh, JPG, Fiji BigDataViewer, HDF5, MRC, NRRD, PNG formatsLoad up to 100 different image and video formatsMicrosoft Excel (export) for quantificationRename and shuffle tool for unbiased classification and segmentationQuantification and StatisticsObjects: Area (2D/3D)Objects: ConvexArea (2D)Objects: Curve Length (2D, pixels and image units)Objects: Eccentricity (2D)Objects: Equatorial Eccentricity (3D)Objects: Equiv Diameter (2D)Objects: Euler number (2D)Objects: Extent (2D)Objects: Filled area (2D/3D)Objects: Holes area (2D/3D)Objects: Length between end points (2D/3D)Objects: Major axis length (2D/3D)Objects: Meridional Eccentricity (3D)Objects: Orientation (2D)Objects: Perimeter (2D)Objects: Second axis length (2D/3D)Objects: Solidity (2D)Objects: Third axis length (3D)Intensity: Correlation (2D/3D)Intensity: Maximal (2D/3D)Intensity: Mean (2D/3D)Intensity: Minimal (2D/3D)Intensity: Standard deviation (2D/3D)Intensity: Sum (2D/3D)MeasurementsAnglesCaliperCircle, radiusFreehand distance and intensity profileLinear distance and intensity profilePolyline distance and intensity profileStereology Wound healing assaySegmentation tools3D ball (3D) 3D lines (3D) Annotations with values Brush tool (2D) Brush tool for 2D superpixels (SLIC , Watershed )Black and White Thresholding tool (global, local, adaptive; 2D/3D) Deep convolutional neural networks for train and prediction Dilate (2D/3D, difference)Drag & Drop Erode (2D/3D, difference)Fill holes (2D/3D)Frame selection tool Frangi tubular filter (2D/3D)Graphcut based semi-automatic segmentation(2D/3D) , Lasso tool (2D/3D) Magic Wand tool (2D/3D) Membrane Click Tracker tool (2D/3D) Morphological operations (branch points, diagonal fill, end points, skeleton, spur, thin, ultimate erosion) Object Picker (2D/3D) Quantification Filtering (2D/3D) Random Forest Classifier (2D/3D)Shape and Line Interpolation (3D) Smooth (2D/3D)Spot tool (2D/3D) Watershed for automatic image segmentation and object separation (2D/3D)Segmentation tools3D ball (3D) 3D lines (3D) Annotations with values Brush tool (2D) Brush tool for 2D superpixels (SLIC , Watershed )Black and White Thresholding tool (global, local, adaptive; 2D/3D) Deep convolutional neural networks for train and prediction Dilate (2D/3D, difference)Drag & Drop Erode (2D/3D, difference)Fill holes (2D/3D)Frame selection tool Frangi tubular filter (2D/3D)Graphcut based semi-automatic segmentation(2D/3D) , Lasso tool (2D/3D) Magic Wand tool (2D/3D) Membrane Click Tracker tool (2D/3D) Morphological operations (branch points, diagonal fill, end points, skeleton, spur, thin, ultimate erosion) Object Picker (2D/3D) Quantification Filtering (2D/3D) Random Forest Classifier (2D/3D)Shape and Line Interpolation (3D) Smooth (2D/3D)Spot tool (2D/3D) Watershed for automatic image segmentation and object separation (2D/3D)Image ProcessingAdd frame around the dataset Alignment Brightness, Contrast, Gamma adjustments Chop and re-chop large dataset to smaller volumesContent-aware fill Contrast-limited adaptive histogram equalizationColor mode change (depth, color type)Color channel operations (add, copy, delete, invert, rotate, shift, swap) Crop , Resize , Flip , Rotate , Transpose Crop 2D/3D objects to files Debris removal Image arithmetics Image filtersIntensity normalization in Z/T (complete slice, masked areas, background shift) Intensity replacement within selected areas Invert Manipulations with slices: insert, copy, delete Intensity projections and focus stacking Morphological operationsVisualizationOrthoslices (XY, ZX, ZY planes) Volume Rendering (hardware) Volume Rendering (software) Models with Matlab isosurfaces Models and volumes with Fiji 3D viewer Models and volumes with Imaris Export models to IMOD Export models to Amira Export models to 3D Slicer Export models and volumes to Matlab Volume Viewer Export models in STL format
For full details of use see article and supplementary information found here: http://pubs.acs.org/doi/full/10.1021/acsnano.5b05968BeanCounter.m is the main script responsible for analyzing EM images. Users can pass images into this script through a graphical user interface (GUI) and the script will output a single excel sheet containing particle statistics. If the user selects an entire directory for analysis, BeanCounter.m will aggregate each individual excel sheet into a single excel document. Users can specify the output location of these excel files using the GUI. The excel sheet contains: minor edge length, minor edge length error, aspect ratio, aspect ratio error, corner rounding, corner rounding error, goodness of fit, area, perimeter, and the shape classification or identifier. All errors are determined by the confidence interval of the fit. The shape identifier is a number that corresponds to a particular shape (i.e. 1: rod, 2: circle, 3: triangle, 4: square, 6: hexagon). The GUI has additional options for specifying output images and processing parameters. Copyright (c) 2015, Chad A. Mirkin, Christine R. Laramy, Keith A. Brown, and Matthew O'Brien.All rights reserved.Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Scientific works derived from this code must include a citation to the paper where it was introduced: M. N. O'Brien, M. R. Jones, K. A. Brown, C. A. Mirkin. Universal Noble Metal Nanoparticle Seeds Realized Through Iterative Reductive Growth and Oxidative Dissolution Reactions. J. Am. Chem. Soc. 2014, 136, 7603 doi: 10.1021/ja503509kHigh-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy ImagesChristine R. Laramy, Keith A. Brown, Matthew N. O’Brien, and Chad. A. Mirkin ACS Nano Article ASAPDOI: 10.1021/acsnano.5b05968. * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution * Neither the name of the nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR ONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Shape and size detection for WBC in blood is necessary for ensuring the ability to eliminate or control the infections or any inflammatory problems in the body.WBC are of five types, subdivided into two parts,1)Granulocytes: neutrophils , basophils, eosinophils2)Agranulocytes: lymphocytes, monocyts.The nucleus of WBC contains DNA and RNA that are stained by hematoxyline in Purple/blue color and the granules in cytoplasm are stained by eosine in pink color. As agranulocytes does not have granules in cytoplasm we won’t see eosine stained image for them. The function deconv is separating out the hematoxyline stained region and eosin stained region in different output images. The function segmentation can be used on this images to segment out nucleus and cytoplasmics granules. Approach:Input RGB image is first deconvoluted to H and E stained Image.Find threshold for each output deconvolved image.Plot histogram representing two regions after segmentation.Apply segmentation based on threshold values.Output image is binary.To see the results download this file, some output images are provided.The segmentation can be done as follows.InputImg = imread('ALL_IDB1\ALL_IDB1\im\Im061_1.jpg'); deconimg = deconv(InputImg);H=deconimg(:,:,1);E=deconimg(:,:,2);[Sh,Hh,Th,Lh]=segmentation(H);[Se,He,Te,Le]=segmentation(E);
This is a simulator able to procedurally create realistic bright-field microscopy images depicting Pap-smears. The principles used in the simulation are described in the paper "Simulation of Bright-Field Microscopy Images Depicting Pap-Smear Specimen" published in Cytometry Part A (http://onlinelibrary.wiley.com/doi/10.1002/cyto.a.22624/abstract).Requires DipImage to run.
Intensities recorded by the microscopes (any imaging system with finite aperture) are subject to diffraction limit - the recorded features cannot be finer than the optical cutoff of the system. Filtering the spatial frequencies beyond the optical cutoff provides simple yet effective means of reducing noise. Since microscopy data is band-limited, padding in frequency domain provides accurate interpolation.
Electron microscope images and image stacks are stored in a proprietary fromat by Digital Micrograph (Gatan Inc.). This function reads version 3 and also the new 64-bit version 4 files. This replaces the ReadDM3 function in my previous post #27021, "Imagic, MRC and DM3 file i/o"
This collection of scripts is intended to correct nonlinear drift distortions in images recorded using any scanning probe microscopy technique (where there is a slow and a fast scan direction). It uses a minimum of two images, with scan directions as close to 90 degrees apart as possible. The first script initializes the data structure, the second performs drift correction and the third generates high quality output images using kernel density estimation. A preprint describing the method can be found here: http://arxiv.org/abs/1507.00320 . The peer-reviewed manuscript can be found here: http://dx.doi.org/10.1016/j.ultramic.2015.12.002 .
This directory contains m-functions for reading and writing files used in electron microscopy and 3D reconstruction. The file formats those used by the IMAGIC software package (Image Science GmbH; EMAN and Frealign are public-domain programs that also use this format), the MRC program library, and the Digital Micrograph (Gatan, Inc.) file format. These functions were written on what published information we could find, and work for our limited purposes.The functions generally assume that little-endian files are being read by a little-endian machine. However, some functions might still work with big-endian machines such as PowerPC, as they were originally written on a Mac computer.Fred Sigworth, Liguo WangYale UniversityReadImagicLoads part or all of the data in an Imagic file pair into a matlab 3-d array. This function also returns a data structure with part of the information from the header.WriteImagicWrites an entire matlab 3-d array as an Imagic file with float32 data type.ReadImagicHeaderMakeImagicHeaderWriteImagicHeaderCreate, read or write a datastructure that contains all the information in an Imagic header. The structure (a struct of arrays) allows manipulation of header information. For writing very large datafiles, the header file can be constructed and written out separately by these functions.ReadDMFile (replaces ReadDM3)Read a file generated by Digital Micrograph, version 3 or 4. These files have a large tree structure, and the code inside this function can be modified to return any field or fields of this structure. At present just the pixel size and number of pixels is returned along with the raw image.ReadMRCRead part or all of an MRC-format file (2d or 3d) into a Matlab array; also retrieves information about the image size.WriteMRCWrite an entire 3d Matlab array into an MRC-format fileWriteMRCHeaderWrite the header of an MRC-format file, allowing the user to write the rest of the file directly with the fwrite function.ReadStarFileReads a STAR file, for example generated by Relion, into a cell array of block names, and a cell array of structures containing the block data.WriteStarFileWrites a MATLAB struct to a STAR file.
This is the software to analysis the atomic force microscopy images. Average feature size and area can be calculated using this software. Steps to follow - 1 - First remove any additional part of the image not included in the scan area using software like paint etc.2 - Browse the image and load it into the software.3 - Move the first (top) slide bar, you will observe the boundary appearing near the patches (AFM features). Select the desired patches.4 - Remove the unwanted small patches by moving the second slidebar.5 - Information like number of patches, average diameter and area will be updated in real time.Note - if open boundary is observed while moving first patch try to increase the boundary parameter.In principle you must be able to select the desired features by playing with these three controls.For further information and suggestions contact me at prashant_patil@live.com
GUI for displaying image stacks, either from a 3D array given as indata or from a tiff, lsm, or other stack chosen by the user.NOTE: StackSlider uses tiffread to import stacks from files. Tiffread can be downloaded for free from www.cytosim.org/other. The GUI features changing of the displayed frame with either a slider or editbox, user-settable colormap, and two smoothing options: Gaussian and averaging (disk) filters. Both smoothing options can be controlled (filter radius and standard deviation of the gaussian) in the GUI. A "reset all" button does exactly what you'd think, and a "make figure" button pops a new figure containing the currently viewed frame including smoothing. Not the coolest thing ever, I know, but I figured someone might find it useful...
Function for creating a volume (array of images) representing topography from a single image.topog=im2topography(afm_im,N,A);N - number of depths levels to describe. The larger N - more slices represents a single image.A - the value of intensity that represents the material. Where no material is present white color (intensity of 256) is used.The topography can then be visualized using visualization routines in MATLAB.
Automatic detection of nanoparticles using hyperspectral microscopyNanoparticles are used extensively as biomedical imaging probes and potential therapeutic agents. As new particles are developed and tested in vivo, it is critical to characterize their biodistribution profiles. We demonstrate a new method that uses adaptive algorithms for analysis of hyperspectral dark-field images to study the interactions between tissues and administered nanoparticles. This non-destructive technique quantitatively identifies particles in ex vivo tissue sections and enables detailed observations of accumulation patterns arising from organ-specific clearance mechanisms, particle size, and the molecular specificity of nanoparticle surface coatings. Unlike nanoparticle uptake studies with electron microscopy, this method is tractable for imaging large fields of view. Adaptive hyperspectral image analysis achieves excellent detection sensitivity and specificity and is capable of identifying single nanoparticles. Using this method, we collected the first data on the sub-organ distribution of several types of gold nanoparticles in mice and observed localization patterns in tumors.Image shows, from left to right: bright field microscopy image of stained section, dark-field microscopy, hyperspectral microscopy, detection of the nanoparticles, shown in orange.This work was published in eLife in Aug 2016: https://elifesciences.org/content/5/e16352.Please cite our paper if you use this code."A hyperspectral method to assay the microphysiological fates of nanomaterials in histological samples".ED SoRelle, O Liba, JL Campbell, R Dalal, CL Zavaleta, A Zerda, eLife, 2016Note: the code is based on images acquired on a Cytoviva microscope that uses Envi for hyperspectral imaging.This project includes Envi reading code from Matlab File Exchange:https://www.mathworks.com/matlabcentral/fileexchange/27172-envi-file-reader-writer
Stain Deconvolution results in separating the contributions of each of the applied stains present in the specimen.Differential staining of cytoplasm, cell nuclei and other cell organelles, and specific proteins provide indicators of the distribution of the substance or structures to which a particular stain is specifically attached.One of the most common examples is the Hematoxylin and Eosin (H&E) staining, with Hematoxylin (blue) mainly staining the cell nuclei, and Eosin (pink) acting as a cytoplasmic stain.After applying the deconvolution function on H&E stained slides image we get separate images of Hematoxylin and Eosin which not only determines staining densities and overall absorption in areas with a specific color, but also determines densities of stains in each area.This estimate of the stain uptake by the slide is important for understanding the density of cytoplasmic and nuclear proteinsFew input images and it's output have been provided in the file.Step 1: Performing Stain deconvolution on an H&E stained imageStep 2: Reconstructing RGB image of different stain
Myelination thickness, axon diameter and the axon-to fiber diameter ratio (G-ratio) is related to neuron dynamic performance. This version 3 program uses the watershed and connectivity theorems to calculate the average g-ratio and the diameter distribution for any given SEM or TEM images.
Analyse Open Microscopy Data in MATLAB®A MATLAB Live Script with accompanyingJupyter® Notebook,m fileandreproducible code capsule on Code Ocean®to access and analyze Microscopy image data sets from the Image Data Resource databaseGet startedUse this tutorial to get started with freely available microscopy data at Image Data Resource directly from MATLAB.No downloads, no installationsOpen directly in MATLAB Online™ by clicking this Step-by-step tutorial shows how toRe-use available data. Access a list of openly available projects on Open MicroscopyQuery and inspect the metadata associated with these projects using commands directly from MATLAB (RESTful API)Avoid downloads. Access specific data from within the database directly and avoid time-consuming downloads of large dataAnalyze image data to identify cellsLet others run your code and reproduce your results quickly. Pubish the results on GitHub and make them accessible using Open With MATLAB OnlineAllow people to cite you! Generate a DOI® for your code by linking your GitHub repository to one of several DOI-generating sites.Live Script contains easy-to-use menus for user to click and select different datasetsAvailable on File Exchange for directly installing onto your MATLAB path with one click using the Add-Ons buttonAccompanying Jupyter notebook (.ipynb) for use in a Jupyter environment. More information on MATLAB kernel hereAccompanying Code Ocean reproducible capsule (.m) for one-click reproducibility of the code by anyone, including reviewers.About the Image Data ResourceThe Image Data Resource (IDR) is a public repository of image datasets from published scientific studies, where the community can submit, search and access high-quality bio-image data.It can be accessed at https://idr.openmicroscopy.org/For advanced users A detailed guide to the Image Data Resource API can be found here. To access the REST API use the MATLAB webread functionRequired ProductsThis tutorial uses the following productsMATLABImage Processing Toolbox ™This code has been developed and tested using MATLAB 2023bNoteThis tutorial works best when delivered by a tutor. It is important to highlight best practices when working with Open Data, publishing Open Code or making research output reproducible
Quick start:0. Set network and training parameters in Params.m.1. Prepare input and output images:Im_In = {Im_In_1,Im_In_2,Im_In_3};Im_Out = {Im_Out_1,Im_Out_2,Im_Out_3};2. Generate training set:Generate_Dataset(Im_In,Im_Out);3. Train:net = Train;4. Apply the trained network to an image:[Im_Out,Im_Label] = Segment_Neuron(net,Im_In_4);imshow(Im_Label);* Example raw and annotated neuron images can be found in this paper [1].* An example pre-trained network is included.* Please cite this paper [1].Advanced options:- Control sample size (see "Input_Size" in Params.m).- Control class weights during training (see "Class_Weights" in Params.m).- Control the minimum number of neuron pixels in training samples (see "Functions" block in Params.m).- You can generate the training set locally and train on another machine (see "Paths" block in Params.m).1. Yuval, O., Iosilevskii, Y., Meledin, A., Podbilewicz, B., & Shemesh, T. (2021). Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans. PLoS computational biology, 17(7), e1009185.
The ceQPM.m is an example script to process the QPM images acquired by the SID4BIO camera using the ceQPM method developed by Xili Liu, et al. The method is described in Liu, Xili, et al. "Computationally Enhanced Quantitative Phase Microscopy Reveals Autonomous Oscillations in Mammalian Cell Growth." bioRxiv (2019): 631119.The image processing pipeline includes:1. load the experiment information2. make the synthetic reference image from the central FOVs3. test the performance the reference by one FOV4. apply the reference to all the images5. remove additional background by the thin plate interpolation6. process the fluorescent images7. cell segmentation8. cell tracking9. save example FOVsSince the processing pipeline varies with the experimental setting, we only give one example on how to process the time-lapse movie taken at multiple positions with multiple channels in a multiple-well plate (t_xy_c). And as such a QPM movie is usually very large, we do not include an example movie in the package. But the functions have been tested by other users with their own data.The background removal parameters are sensitive to the magnification and cell area. The segmentation and tracking parameters are sensitive to the cell line. The parameters used in the example are specific for HeLa cells measured under the 10X objective.For clarification, we only include the functions called by the example script in this package. One can easily modify the example functions to work for other experimental settings. Functions for other experimental settings or other cell lines are also available upon request.Created by Xili Liu Last modified on 4/21/2020contact information: Xili_Liu@hms.harvard.eduThis toolbox uses the array_padd function developed by Sergei Koptenko; imOverlay developed by David Legland; parseXML; parseChlidNodes; real2rgb and rescale developed by Oliver.Woodford.
Retrieval of accurate phase maps from Digital Holographic Microscopy (DHM) is highly dependent on the representation of the carrier fringe pattern in the digital sensor. Physically, the contrast of the fringes is given by the degree of coherence of the illumination (), the intensity relation between the interferometer arms (), the modulation transfer function of the sensor (), and the local transmittance of the sample (). However, once digitalized, it also depends on the bit-depth of the camera's ADC.This script allows the user to establish the aforementioned parameters and predict the lower limit of the allowable transmittance for each possible bit-depth of the sensor between 1-bit and 16-bit.Further details regarding the theory supporting this tool can be found at https://doi.org/10.1016/j.optlaseng.2023.108002 (Buitrago-Duque, C., Garcia-Sucerquia, J., Martínez-Corral, M., & Sánchez-Ortiga, E. (2024). Revisiting the sample transmittance and camera bit-depth effects on quantitative phase imaging in off-axis digital holographic microscopy. Optics and Lasers in Engineering, 175, 108002)
Matlab GUI for automated detection of fluorescent cells in In-Resin Fluorescence sections for Integrated Light and Electron Microscopy
NanoLocz Atomic Force Microscopy Analysis Platform.01.03.2024 - Full Article Released in Small Methods: https://doi.org/10.1002/smtd.20230176601.03.2024 - NanoLocz Version 1.10 Update:General: bug fixes, more colormaps, stacking of images/video with different pixel dimensions.File Openers: .asd file heights corrected.Simulation AFM: Parachuting option added.LAFM: Improved localization precision, improved auto centre for symmetrisation, height scale in nm.New filter: remove scars/scratches.23.11.2023 - Preprint Released on BioRxiv - Link Full Software version 1.0 released - Go to Releases to download the latest version Includes several updates to file openers, stability, speed and new features.About NanoLoczNanoLocz is a free interactive AFM image viewer and analysis platform. It's designed for browsing and analysingheight data from AFM and HS-AFM imaging with the aim of increasing throughput. see User Guide here!Capabilities:Read AFM file types: .spm, .asd, .jpk, .ibw, .ARIS, .tiff, .nhf, .gwyImage LevellingLine ProfilingVideo AlignmentSimulation AFMMask AnalysisParticle Detection (height or reference based)Single Particle TrackingParticle AlignmentLocalization AFMExport as: .tiff, .gif, .avi, .png, .jpeg, .pdf, .txt, .csv, .xlsCompatible file types: .spm, .asd, .jpk, .ibw, .ARIS, .tiff, .nhf, .gwy.... more being addedplease request (to g.r.heath@leeds.ac.uk) if your preferred file format is not hereLink to User GuideNanoLocz Install - Go to Releases:Note: loading on start-up of the app can take ~1min for Mac/Windows versionsOptions for Install:Install as Mac desktop app download: Mac_AppInstaller_mcr.app.zipInstall as Windows desktop app download: Windows_AppInstaller_mcr.exeInstall as MATLAB app download: NanoLocz.mlappinstallOpen 'NanoLocz.mlappinstall' file. This will open MATLAB if not already open and ask you to install.Once installed the app can be used from the apps tab. For quick access right click the NanoLocz app to add toFavourites and/or Quick Access Toolbar.MATLAB app requirements: MATLAB 2020a (the newer the better), Curve Fitting Toolbox, Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, Bioinformatics Toolbox, Computer Vision Toolbox.Disclaimer:THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS ORIMPLIED,INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR APARTICULARPURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERSBE LIABLEFOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE ORTHEUSE OR OTHER DEALINGS IN THE SOFTWARE.ContributingContributions are extremely welcome.LicenseDistributed under the terms of the GNU GPL v3.0 license,"NanoLocz" is free and open source softwarePublicationsIf using the NanoLocz software please cite:Heath, G.R, Micklethwaite, E. and Storer, T.M.NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM.Small Methods 2024, 2301766.If using the Localization Atomic Force Microscopy method please cite:Heath, G.R., Kots, E., Robertson, J.L. et al.Localization atomic force microscopy. Nature 594, 385–390 (2021)
Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. We propose a more flexible algorithm that adaptively chooses the basis based on the data. Because the basis adapts to the data, the reconstruction resolution and signal-to-noise ratio (SNR) is improved compared to a reconstruction with a fixed basis. Moreover, the algorithm automatically masks the particle, thereby separating it from the background. This eliminates the need for ad-hoc filtering or masking in the refinement loop. The algorithm is formulated in a Bayesian maximum-a-posteriori framework and uses an efficient optimization algorithm for the maximization. Evaluations using simulated and actual cryogenic electron microscopy data show resolution and SNR improvements as well as the effective masking of particle from background. These files provide a MATLAB implementation of our algorithm with a small simulated cryo-EM dataset for testing.
Single particle reconstruction methods based on the maximum-likelihood principle are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of servers. To address this problem, we have developed a new algorithm called SubspaceEM for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude, and the new algorithm produces similar quality reconstructions compared to the traditional maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy images and the structure projections, greatly reducing the number of image transformations and comparisons that are computed. The files include an implementation of the SubspaceEM algorithm. The main script is subspaceEM.m. In addition, a small dataset for testing is included. Please view the readme PDF for further details.
Segment the blood vessels from a dynamic image of fluorescent microscopy. == Install ======- Add all attached files to matlab path- Download "Better Skeletonization" from following URL and add to matlab path http://www.mathworks.com/matlabcentral/fileexchange/11123-better-skeletonization== Instruction =========1. Save time lapse images by tiff format in a directory. The alphabetical order of file name must correspond to the order of time frame. 2. Read Tiff format files in a directory and save it in a matlab file. >> imgData = VBSreadTiff('directory name');Here, "imgData" is a structure of x,y,t image and the header of tiff.3. Lounch VesselBranchSegmentation>> VesselBranchSegmentation4. In Menu, Select "File > New", then select a saved matfile.5. In Menu, Select "Estimation > Vessel Mask", then vessel region is extracted from vessels.(*)6. In Menu, Select "Estimation > Vessel Class", then vessel region is classified into artery and vein.(*) This process takes a bit long time (~ 1 hour).7. In Menu, Select "Estimation > Segmentation to Branches". New window appears and skeleton of artery mask is calculated. (**) Then press "To branch" button for segmentation to vessel branches. After closing the skeleton-shown window, repeat the same process for vein region.(*) The extracted mask can be modified by the edit tool. Turn "Editable checkbox" on to use the edit tool. See the document of impoly function for details.(**) The undesired skeleton will be calculated for low SNR images because of the ambiguous edge of vessel. The skeleton can be manually modified by the edit tool in the window.
Read a tiff image stack:I = ReadTiffStack('absolute file name');ViewImageStack(I);
Two scripts for the analysis of platelet aggregation in microfluidic channels after whole blood perfusion. The first script, “Platelet_Coverage”, automatically corrects for channel misalignment, determines a threshold using the triangle method, creates a black and white image representing platelet coverage and finally determines the platelet coverage. The second script, “Clot_Properties”, uses the black and white image from the first script to determine the platelet aggregate size distribution.Triange Thresholding is done using a script of dr. Panneton (MathWorks, #28047) which is based on the original work of Zack, Gregory W., William E. Rogers, and S. A. Latt. "Automatic measurement of sister chromatid exchange frequency." Journal of Histochemistry & Cytochemistry 25.7 (1977): 741-753.
MATLAB code for a Graphic User Interface dedicated to automatically count and quantify dendritic spines from fluorescence microscopy images. It has been tested with .oib files from immunohistochemistries standard protocols. Quantification is not perfect, but it can be fine-tuned for particular sets of images and it's going to be always reproducible, as opposed to the work of manual operators who usually perform this tasks.
SMLM_interface_detectionCode for detecting interfaces in super-resolution microscopy (SRM) data,typically single-molecule localization microscopy (SMLM).The code provided here is under Copyright © 2021 Dingeman van der Haven and Pim van der HoornThis method is explained in detail in the publication "Parameterless detection of liquid-liquidinterfaces with sub-micron resolution in single-molecule localization microscopy", 2022,https://doi.org/10.1016/j.jcis.2022.03.116Brief explanation for using the scripts for determining the MLE for the boundary between tworegions of a Poisson Point Process with different densities in each region.The model assumption is that the points come from a Poisson Point Process on a rectangular region.This region has been divided into two regions by a straight line through the point [a] and [b]and the Poisson process is homogeneous in both regions but with different density values. Seefigure below:a------------------------|\|| \|| \ mu2|| \|| mu1 \|| \|| \|------------------------bTo generate an instance of the Poisson Point Process use [generatePoisson2D]. Here you need tospecify the points [a] and [b] for the line that separates the two regions. You also need tospecify the expected number of points [M] and the relative density [delta] between mu1 and mu2.The main function is [mleBoundaryEstimation]. This computes the maximum value of the loglikelihoodestimator and outputs the estimated separation line that maximizes the loglikelihood as well asthe maximum value of the likelihood function.To compute the MLE the function only considers lines through pairs of points(p1, p2) that lie in agiven top bandwith and bottom bandwidth:top bandwidth------------------------| ||| |||_____||| || _______||||||| ||------------------------ bottom bandwidthBoth the boundaries of the top bandwidth and the bottom bandwidth can be specified using theoptional second and third argument of [mleBoundaryEstimation].There are two main computation modes available that determine which points in the specifiedboundaries are considered. These can be set via the [IterationMethod] parameter:Steps:For this the parameter [IterationMethod] should be set to 'steps'. This is also the default settingfor [mleBoundaryEstimation]. In this mode the boundaries of both the top and bottom bandwidth thatintersect with the boundaries of the region, are partitioned into equal size intervals. This means,for example, that if the provided bandwidth overlaps with the left and top boundary of the regionthe left boundary of the bandwidth and the top boundary will be partitioned.The number of intervals can be specified using the [IterationSteps] parameter. If [IterationSteps]is set to M, then there will be M+1 points (the +1 is for one of the boundaries). The defaultsetting is 50.The MLE estimation will then consider lines that go through the pair of points (p1, p2), wherep1 is one of the interval boundaries for the partitioned top bandwidth and p2 an interval boundaryin the bottom bandwidth p1|-|-|-|-----------------T || p1 T ||T_____||| || _____|| | ||| | ||-----------|-|-|-|------ p2Points:For this the parameter [IterationMethod] should be set to 'points'. With this setting the MLE iscomputed by considering lines that go through pairs of points (p1, p2), where p1 and p2 are pointsof the Poisson process in the top and bottom bandwidth, respectively.------------------------| ||| p1 |||_____||||| _______||||||| p2 ||------------------------The script [testBoundaryEstimation] provides a minimal code example for running the estimationprocedure.Note: The script mleBoundaryEstimationParticle currently does not account for overlap between theparticles at the interface. If the particles at the interface overlap, the overlap area between twoparticles will be subtracted from the left and/or right area twice, which should not happen. Thisneeds to be corrected in a future version by checking all particle pairs for overlap and then makingsure that the overlap between the particles is only subtracted once.
Hyperspectral CARS microscopy and spectroscopy toolbox allows researchers easy analysis of their data.Toolbox focuses on image fusion, denoising and spectroscopy.
Segment axon and myelin from microscopy data. Written in Matlab. The compiled versions are also available for those who do not have the necessary processing toolboxes.
Registration, cell detection, spike extraction and manual GUI. Can process data from 10,000 simultaneously-recorded neurons in approximately real-time for several hour recordings. Details in http://biorxiv.org/content/early/2016/06/30/061507.
See also http://dylan-muir.com/articles/tiffstack/If this code is useful to your academic work, please cite the publication in lieu of thanks: Muir and Kampa, "FocusStack and StimServer: A new open source MATLAB toolchain for visual stimulation and analysis of two-photon calcium neuronal imaging data". Frontiers in Neuroinformatics 2015. Usage: tsStack = TIFFStack(strFilename <, bInvert>)A TIFFStack object behaves like a read-only memory mapped TIF file. The entire image stack is treated as a matlab tensor. Each frame of the file must have the same dimensions. Reading the image data is optimised to the extent possible; the header information is only read once. This class attempts to use the version of tifflib built-in to recent versions of Matlab, if available. Otherwise this class uses a modified version of tiffread [1, 2] to read data. Code is included (but disabled) to use the matlab imread function, but imread returns invalid data for some TIFF formats.permute, ipermute and transpose are now transparantly supported. Note that to read a pixel, the entire frame containing that pixel is read. So reading a Z-slice of the stack will read in the entire stack.Construction:>> tsStack = TIFFStack('test.tiff'); % Construct a TIFF stack associated with a file>> tsStack = TIFFStack('test.tiff', true); % Indicate that the image data should be invertedtsStack = TIFFStack handle Properties: bInvert: 0 strFilename: [1x9 char] sImageInfo: [5x1 struct] strDataClass: 'uint16'Usage:>> tsStack(:, :, 3); % Retrieve the 3rd frame of the stack, all planes>> tsStack(:, :, 1, 3); % Retrieve the 3rd plane of the 1st frame>> size(tsStack) % Find the size of the stack (rows, cols, frames, planes per pixel)ans = 128 128 5 1>> tsStack(4); % Linear indexing is supported>> tsStack.bInvert = true; % Turn on data inversionSupport for de-interleaving of channels and slices into the frame axis is also supported (see help text for TIFFStack).References:[1] Francois Nedelec, Thomas Surrey and A.C. Maggs. Physical Review Letters 86: 3192-3195; 2001. DOI: 10.1103/PhysRevLett.86.3192[2] http://www.cytosim.org/misc/
There are two functions in the ZIP file. One of them (extractZeissRois) extracts ROIs from a Zeiss CZI file opened by the bfopen function of Bio-Formats. Information about the ROIs is saved in a structure variable.The second function (drawZeissRois) draws the ROIs stored in the structure variable.The displayed image shows eight ROIs in the ZEN program and in an image window in which ROIs extracted and displayed by the two programs in the ZIP file are shown.function allRois=extractZeissRois(bfImage)The function extracts ROIs from a Zeiss CZI file opened by Bio-Fomats. The CZI image file must be opened by the 'bfImage' function of Bio-Formats. This is the input to the extractZeissRois function (bfImage). The ROIs are stored in a structure variable (allRois). This structure variable can be used by the drawZeissRois function to display the ROIs.function roiImage=drawZeissRois(varargin)The function displays ROIs extracted from a Zeiss CZI file by the extractZeissRois function.roiImage=drawZeissRois(roiStructure, imageSize, outputType, filled, separateImages)ORroiImage=drawZeissRois(roiStructure, imageSize) defaults to 'dipimage' output type, filled ROIs in one image.roiStructure - a structure variable generated by extractZeissRoisimageSize - a two-element array: [xSize,ySize], i.e. [horizontalSize,verticalSize]outputType - 'dipimage' or 'matlab' corresponding to a dip_image or a matlab numeric array, respectivelyfilled - 1 or 0 corresponding to filled ROIs or only their circumferenceseparateImage - 1 if each ROI is to be saved in a separate image. Otherwise all ROIs will be drawn in the same image.
This script corrects for spatial drift between frames of video data using a cross-correlation algorithm. It can take video data as input, and then it will output a video file with the features in each image frame aligned. An instruction manual has been submitted to Microscopy Today and will hopefully be published in the near future.
Read and preview of Leica's image format file. -- Usage --1. In matlab prompt,>> HKloadLifGUI window to pick a file will open.2. Select a Leica image format (.lif) file. 3. A list of all images in the file will appear.- Turn check box on for preview.- Press export button in a preview window to export the image data in work space. The image is exported as a structure. ExportedImage = Image: {2x1 cell} % Image with several Channel Info: [1x1 struct] % Image info Name: 'Series001' Type: 'X-Y-Z' NumberOfChannel: 2 Size: '512 512 220'-- Attention--Please do not chenge the file location duaring a table is open.
SimpleMScanner is a collection of example tutorial code showing how to write acquisition software for a laser-scanning microscope. It will work with any scanning system from a transmitted light microscope with a photodiode, up to a 2-photon microscope. SimpleMScanner is not designed to be a complete application, but rather a teaching aid or perhaps even a basis upon which to begin writing a complete application. Examples are written for National Instruments acquisition hardware using both TMW Data Acquisition Toolbox and with a slightly lower level NI DAQmx wrapper. For more information, please click "Learn More" to see the project Readme on GitHub.
This script allows the user to load 3-D TIFF images, such as those derived from confocal or 2-photon microscopy, into the MATLAB workspace for analysis of colocalization between two images in 3-D. It includes a function that is used for loading the images using the Windows file browser via . The script defines the colocalization after thresholding the images by computing the logical AND of the two images. Label matrices are used to define "particles," which typifies the signal seen in cell biology immunolabeling experiments.A number of useful variables are created that describe the particles and their colocalization, including the number, size, intensity, etc. All the output variables are clearly defined by comments in the code.
An automated data analysis method for atmospheric particles using scanning transmission X-ray microscopy coupled with near edge X-ray fine structure spectroscopy (STXM/NEXAFS). This method is applied to complex internally mixed submicron particles containing organic and inorganic material. Several algorithms were developed to exploit NEXAFS spectral features in the energy range from 278-320 eV for quantitative mapping of the spatial distribution of elemental carbon, organic carbon, potassium, and non-carbonaceous elements in particles of mixed composition. This energy range encompasses the carbon K-edge and potassium L2 and L3 edges. STXM/NEXAFS maps of different chemical components were complemented with a subsequent analysis using elemental maps obtained by scanning electron microscopy coupled with energy dispersive X-ray analysis (SEM/EDX). We demonstrate application of the automated mapping algorithms for data analysis and the statistical classification of particles.
Matlab function to import ROIs from ImageJ.
What is Gwyddion?"Gwyddion is a modular program for SPM (scanning probe microscopy) data visualization and analysis. Primarily it is intended for analysis of height fields obtained by scanning probe microscopy techniques (AFM, MFM, STM, SNOM/NSOM) " (http://gwyddion.net/)saveasgsf saves a NxM Matrix in the Gwyddion Simple Field file format (.gsf) (one Channel only)saveasgwy saves a NxM or NxMxL Matrix in the Gwyddion native file format (.gwy)Function call:saveasgsf(filename,data,numstepsx,numstepsy,startx,endx,starty,endy,label,unit,time,varargin)orsaveasgwy(filename,data,numstepsx,numstepsy,startx,endx,starty,endy,label,unit,time,varargin)Examples:saveasgsf('test.gsf',rand(30,200),200,30,1,2,3,4,'Chan1','V',now);saveasgsf('test.gsf',rand(30,200),200,30;saveasgwy('test.gwy',rand(30,200,2),200,30,1,2,3,4,{'test1','test2'},{'V','m'},now);Want to help to improve the code?=> https://github.com/jensb89/Matlab2Gwyddion
This matlab code implements Tamura Coefficient to find out the best infocus image in the stack of the images.The stack of the microscopy has many images but one of the image is the best infocus.Certain application need to find out the best infocus image in the stack of the image.Tamura coefficient takes into consideration the standard deviation and mean of the image.The minimum of this coefficient represents the best infocus image in the stack of the image.
This example illustrates the possibility to control the ZEN Blue microscope control software from within a MATLAB script via the COM interface. This allows using the ZEN Python scripts as an integral part of an M-File.The ZEN OAD (Open Application Development) API can be imported into MATLAB and allows automating the complete workflow including the image acquisition from MATLAB.- ZEN is used as an image acquisition machine- the experiment is started directly from within MATLAB- the resulting CZI file is imported via BioFormats- the image analysis is done in MATLAB
This algorithm is used to reconstruct 3D FRET data generating high-resolution 3D maps of total fluorophore concentration as well as protein-protein interactions.
In surface roughness analysis, one of the powerful tools for roughness characterization is surface roughness power spectrum. If the surface under study has isotropic roughness characteristics, then one can do a radial average on the calculated discrete Fourier transform of the surface topography and obtain its 2D power spectrum, namely, 2D PSD.With this function you can calculate 2D PSD of a surface topography, which the topography is normally obtained by any 3D profilometry techniques, such as AFM (Atomic Force Microscopy), WLI (White Light Interferometry) and many other optical profilers. As inputs, you first need to have a matrix (n by m) of your height values (z). Second, you need to know you PixelWidth (spatial resolution) which is obtained easily by dividing you image length to the number of pixels in length, i.e. :PixelWidth = Lx / m; % in SI unitsIn order to plot the output:loglog(q,C)In order to check 2D FFT of your topography:imagesc(1+log10(abs(PSD.Hm)))An extended version of this code, calculates the surface roughness power spectrum only for a portion of the top or bottom part of the surface roughness/topography. This has been uploaded in here:Radially averaged surface roughness power spectrum (PSD) only on top or bottom part of a topography http://se.mathworks.com/matlabcentral/fileexchange/60771-radially-averaged-surface-roughness-power-spectrum--psd--only-on-top-or-bottom-part-of-a-topographyTo calculate 1-dimensional suface roughness power spectrum, refer to:1-Dimensional surface roughness power spectrum of a profile or topographyhttp://se.mathworks.com/matlabcentral/fileexchange/54315-psd-1d-z--pixelwidth--dim-To generate an artificial randomly rough surface/topography to try these codes, refer to:Surface generator: artificial randomly rough surfaceshttp://se.mathworks.com/matlabcentral/fileexchange/60817-surface-generator--artificial-randomly-rough-surfaces
This is a MATLAB script collection developed at Lawrence Berkeley National Lab that can be used as a basis for Scanning Transmission X-ray Microscopy (STXM) data analysis. It includes routines for the following basic tasks:- raw stack data import- image alignment- automated conversion to optical density (OD)- stack movie playback - quantitative component mapping using Singular Value Decomposition (SVD) techniquesThis package also contains the GUI stack exploration tool STACKLab. It allows the extraction of average spectra from arbitrary regions of interest (ROI) and the creation of 2 image difference-maps. All results can be exported as figures or text files for further use.
This script acts to import files from Gatan's .DM3 file format, utilized for electron microscopy, into a MATLAB structure. The fields of the MATLAB structure can then be referenced with the dot-operator.For example, one can load and display a file with the appropriate scaled axes in nanometers and electrons with the following example script: dm3struct = DM3Import( 'RandomBrightfieldImage.dm3' ); N = length( dm3struct.image_data ); imagesc( (1:N).*dmstruct.xaxis.scale (1:N).*dmstruct.yaxis.scale, ... dm3struct.image_data.*dm3struct.intensity.scale );This script currently imports images, EELS spectra, and spectral images. Now imports annotations (text) written on the image as well.This script was constructed largely by parsing example DM3 files with a hex editor. It uses regular expressions to find the tags that indicate the start points of the various fields within the files, and then strips the data. To the best of my knowledge there is not existing documentation on the actual object used to write DM3 format files so it is not practical to output to DM3 format.
Matlab implementation of a source extraction and spike inference algorithm for large scale calcium imaging data analysis, based on a constrained matrix factorization approach (CNMF).
This toolbox includes routines for using principal component analysis (PCA) and independent component analysis (ICA) to extract cellular signals from imaging data sets. A full description and validation of the method is provided in the paper, "Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data," Neuron, 63:747 (2009): http://tinyurl.com/cellsort
ObjectFinder is a MATLAB app that allows you to recognize a large number of small structures within a three-dimensional image volume. This app is developed for neuroscience research, with the purpose of detecting fluorescently-labeled synapses in neuronal image stacks acquired using confocal or super-resolution microscopes.Key features: - Multi-threaded 3D object connectivity search within large image volumes - Trainable deep learning classifier for automatic validation of objects- Visual interaction with objects using the builtin volume inspector - 3D inspection and interaction of detected objects using Bitplane Imaris- Automated colocalization analysis - Automated nearest neighbor analysis- Integrated plots of detected object's statistics - Export analysis results to Microsoft Excel® - Batch processing of multiple images with custom start time For more information and to download latest ObjectFinder version visit: https://lucadellasantina.github.io/ObjectFinder/
PatchWarp is an image processing pipeline for neuronal calcium imaging data. It can correct complex image distortions that slowly occur during long imaging sessions. First, the pipeline performs rigid motion corrections by iterative re-estimation of template images. Then, the imaging field is split into user-specified number of subfields. A gradient-based algorithm independently finds the best affine transformation matrix for each frame of each subfield to correct the across-time image distortion of each subfield. The distortion-corrected subfields are stitched together like patchwork to reconstruct the distortion-corrected whole imaging field. PatchWarp can be also used to register images from different imaging sessions for longitudinal activity analyses.Before (Left) and after (Right) PatchWarp warp correction for within-session image distortions(2.25 hrs in vivo 2-photon calcium imaging of cell bodies with complex distortions)(19 min in vivo 2-photon calcium imaging of axons with complex distortions. Image credit to Chi Ren.)Before (Left) and after (Right) PatchWarp across-session image registration (A later imaging session (cyan) was registered to an earlier imaging session (red))InstallationDownload files from this github repository, and add all files to your MATLAB path. The code does not work properly on MATLAB2023. Please use older MATLAB version.**Note that PatchWarp uses Parallel Computing Toolbox. The toolbox needs to be installed to run PatchWarp code.How to usePlease check example demo files.For within-session distortion correction, please check patchwarp_demo.m.Example data to run the demo code is available at https://doi.org/10.6084/m9.figshare.19212501.v1For across-session image registration, please check patchwarp_across_sessions_demo.m.Example data to run the demo code is available at https://doi.org/10.6084/m9.figshare.19217421.v1CitationExample citation format for the preprint:Hattori, R. and Komiyama, T. PatchWarp: Corrections of non-uniform image distortions in two-photon calcium imaging data by patchwork affine transformations. Cell Reports Methods (2022), https://doi.org/10.1016/j.crmeth.2022.100205.Software DOI from Zenodo:
Navigate large (gigabytes) 3-D image stacks and trace axons or dendrites. This is a plugin for the free MATLAB-based large volume viewer, MaSIV. It adds to MaSIV the ability to manually trace an axon's neurites. Please see the plugin's GitHub page for install instructions (you will need MaSIV): https://github.com/raacampbell/neuriteTracer
This script supports "axon analysis" by determining the average baseline variability across a number of independent experiments. The full protocol is described in detail in Hallinan et al., Bio-Protocol 2020.
DiffractIndex (created in Guide) imports tif, jpg, bmp or dm* (Digital Micrograph) files for analysis of diffraction ring diameters in 2D diffraction patterns. DiffractIndex calculates diffraction ring centres and the pattern centre to output ring radii in Å given that pixel size calibration is performed. DiffractIndex can be used to calibrate pixel size by using a known standard diffraction pattern. The program also generates a 1D diffractogram by azimuthally integrating (caking) the diffraction pattern. See the pdf file for full documentation.
This package includes a variety of scripts for analysis of microscopy images, using the SAFIRE screening platform. This package is useful for analyzing high-content screens of intracellular bacteria. The package inputs images from an ArrayScan microscopy, segments host cells, and quantifies intracellular bacteria. It applies B-score normalization to the resulting data.
This is a Nikon ND2 file reader. It's implemented through scanning ND2 files for meaningful information to reconstruct file specs. The nd2read is a sample implementation that demonstrates one possible way to read an image file with three channels.
For atomic force microscopy force curve analysis within Matlab. Reads Asylum Research Data Files (.ARDF).readARDF() reads image, note, and other data from the ARDF file into a Matlab structure.getARDFdata() reads a single force curve or line of force curves from an ARDF file into Matlab.These two scripts aim to keep most of the ARDF data in the ARDF file. These ARDF files are often 1 GB or larger. Why copy or convert all of the file contents if you don't have to?I have been using these files for years now. I believe I've worked out most of the bugs. They are based on my deciphering of the ARDF file protocol, which is proprietary and may change.
A good tool to display all kinds of 3D image stacksLSM (Laser scanning microscopy) imagesCT scan (x-ray) imagesMRI imagesConfocal microscopy imagesOCT (optical coherence tomography) images
Segment cells from microscopy images using the Medical Imaging Toolbox™ Interface for Cellpose Library. The support package provides functionality for downloading pretrained models from the Cellpose Library [1,2], segmenting 2D and 3D images using Cellpose models, and training new models using your own image data. To learn more, see the Getting Started with Cellpose documentation page. Using the support package, you can: Configure a Cellpose model by using a cellpose object.Segment 2D images by using the segmentcells2D function. Segment 3D images by using the segmentcells3D function.Train a custom Cellpose model by using the trainCellpose function.Download and save all pretrained models from the Cellpose library by using the downloadCellposeModels function. The support package documentation also features examples to help get started with workflows such as selecting and tuning a pretrained Cellpose model, and segmenting large whole slide images (WSI) using Cellpose.To learn more about the Cellpose Library, visit the Cellpose web site and the Cellpose GitHub® repository. References [1] Stringer, Carsen, Tim Wang, Michalis Michaelos, and Marius Pachitariu. “Cellpose: A Generalist Algorithm for Cellular Segmentation.” Nature Methods 18, no. 1 (January 2021): 100–106. https://doi.org/10.1038/s41592-020-01018-x. [2] Pachitariu, Marius, and Carsen Stringer. “Cellpose 2.0: How to Train Your Own Model.” Nature Methods 19, no. 12 (December 2022): 1634–41. https://doi.org/10.1038/s41592-022-01663-4.
Cells were segmented using a custom-made image processing pipeline. The segmentation pipeline was implemented in order to distinguish cells from the background. The segmentation pipeline is composed of standard image-processing operations in the following order: 1, original image; 2, Sobel edge detection; 3, image dilation; 4, removal of objects close to image borders; 5, image erosion; 6, removal of small objects; 7, filling of gaps inside the cell; and 8, overlay of the final result on the original image. Seven morphological features were extracted from each of the segmented cells. The feature space in which we performed statistical classification was therefore seven-dimensional (7D; one vector for each cell), with the following features: area, major and minor axis lengths, perimeter, eccentricity, extent, and number of fingers (Gorelick, PAMI, 2006). Statistical analysis was performed on the 7D feature vectors, using a tree-like classification method called the ’node harvest’ method, which was introduced by Meinshausen, Annals of Applied Statistics, 2010.
SPIDER is a free image processing system for electron microscopy. It is used for three-dimensional reconstruction of single particle macromolecules, multivariate statistical classification, and electron tomography. See the extensive documentation and many available techniques at www.wadsworth.org/spider_doc/spider/docs/master.htmlSPIDER has its own binary format for images and volumes. The SPIDER M-file collection lets users read SPIDER images, volumes, image stacks, and textual document files directly into Matlab data types for processing and visualization.