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matlab function graythresh  (MathWorks Inc)


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    MathWorks Inc matlab function graythresh
    Matlab Function Graythresh, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlabs+graythresh+function/pmc02634994-126-60-59
    Average 90 stars, based on 1 article reviews
    matlab function graythresh - by Bioz Stars, 2026-11
    90/100 stars

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    Article Title: Study and Performance Evaluation of Brain MRI Images Using Aartificial Intelligence
    Article Snippet: MATLAB employs the graythresh function, which computes a global threshold that can be used to convert an intensity image to a colour image. level = graythresh(I) It computes a global threshold (level) for converting an intensity image to a binary image.

    Article Title: Drought stress prediction and propagation using time series modeling on multimodal plant image sequences
    Article Snippet: In this paper, we used graythresh() function of Matlab to generate the global threshold followed by imbinarize() to create the binary mask.

    Article Title: Mechanical waves identify the amputation position during wound healing in the amputated zebrafish tailfin
    Article Snippet: Highly regenerative animals can regrow lost appendages and the rate of regrowth is proportional to the amount of appendage loss.. This century-old phenomenon prompted us to investigate whether the mechanism of wound healing, as the first stage of regeneration, is responsible for discerning the amputation position.. In vitro studies have revealed great insights into the mechanics of the wound-healing process, including the identification of mechanical waves in collective epithelial cell expansion.

    Article Title: Experimental Study: The Effect of Pore Shape, Geometrical Heterogeneity, and Flow Rate on the Repetitive Two-Phase Fluid Transport in Microfluidic Porous Media.
    Article Snippet: These cropped images were then converted into binary images using the graythresh function in MATLAB.

    Article Title: Void Volume Fraction of Granular Scaffolds.
    Article Snippet: Users input a parameter, γ that divided the output of the MATLAB function graythresh (Otsu algorithm) used for binarization. γ = 1 or 1.5 was chosen for the low output category, γ = 2 for the middle output category, and γ = 2.5 or 3 for the high output category.

    Article Title: Initiation of lumen formation from junctions via differential actomyosin contractility regulated by dynamic recruitment of Rasip1
    Article Snippet: Image thresholding was performed using an automatic thresholding method based on Otsu’s algorithm, implemented through the graythresh function in MATLAB.

    Article Title: A comparative study of histotripsy parameters for the treatment of fibrotic ex-vivo human benign prostatic hyperplasia tissue
    Article Snippet: These images were thresholded and converted to binary images using Otsu’s method (graythresh function in MATLAB), and areas below the threshold were considered part of the disrupted tissue.

    Membrane:

    Article Title: Automated quantitative analysis of epithelial cell scatter
    Article Snippet: .. 12 fig ft0 fig mode=article f1 Figure 4 caption a4 Automated image processing using MATLAB. (A and A') Nuclear- and membrane-localized fluorescent proteins, H2B-GFP and mCherry respectively, were co-expressed in MCF-10A cells. (B and B') Epifluorescence images of scattering cells were segmented using ... To identify cell clusters, a global threshold was applied to red-channel intensity images using the MATLAB function graythresh. ..



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    Quantification of filament numbers and lengths. (A) Flow-chart summarizing image processing and quantification steps. (B) Representative micrograph of filaments polymerized from 2 µM actin monomers, labeled with FITC-phalloidin, diluted into microscopy buffer, and imaged using TIRF microscopy. (C-E) Micrograph shown in B following sequential (C) noise filtering and background subtraction, (D) normalization and image <t>thresholding,</t> and (E) binarization and skeletonization. (F) (Left) Following image processing, overlapping filaments are detected as errors and highlighted in red. (Right) Each misidentified filament is processed individually until all errors are resolved, and all filaments are highlighted in green. (G) Histogram of filament lengths measured for the representative micrograph shown in panel B. The bin size is 3 µm.
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    Quantification of filament numbers and lengths. (A) Flow-chart summarizing image processing and quantification steps. (B) Representative micrograph of filaments polymerized from 2 µM actin monomers, labeled with FITC-phalloidin, diluted into microscopy buffer, and imaged using TIRF microscopy. (C-E) Micrograph shown in B following sequential (C) noise filtering and background subtraction, (D) normalization and image <t>thresholding,</t> and (E) binarization and skeletonization. (F) (Left) Following image processing, overlapping filaments are detected as errors and highlighted in red. (Right) Each misidentified filament is processed individually until all errors are resolved, and all filaments are highlighted in green. (G) Histogram of filament lengths measured for the representative micrograph shown in panel B. The bin size is 3 µm.
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    Quantification of filament numbers and lengths. (A) Flow-chart summarizing image processing and quantification steps. (B) Representative micrograph of filaments polymerized from 2 µM actin monomers, labeled with FITC-phalloidin, diluted into microscopy buffer, and imaged using TIRF microscopy. (C-E) Micrograph shown in B following sequential (C) noise filtering and background subtraction, (D) normalization and image <t>thresholding,</t> and (E) binarization and skeletonization. (F) (Left) Following image processing, overlapping filaments are detected as errors and highlighted in red. (Right) Each misidentified filament is processed individually until all errors are resolved, and all filaments are highlighted in green. (G) Histogram of filament lengths measured for the representative micrograph shown in panel B. The bin size is 3 µm.
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    graythresh matlab function  (MathWorks Inc)
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    Quantification of filament numbers and lengths. (A) Flow-chart summarizing image processing and quantification steps. (B) Representative micrograph of filaments polymerized from 2 µM actin monomers, labeled with FITC-phalloidin, diluted into microscopy buffer, and imaged using TIRF microscopy. (C-E) Micrograph shown in B following sequential (C) noise filtering and background subtraction, (D) normalization and image <t>thresholding,</t> and (E) binarization and skeletonization. (F) (Left) Following image processing, overlapping filaments are detected as errors and highlighted in red. (Right) Each misidentified filament is processed individually until all errors are resolved, and all filaments are highlighted in green. (G) Histogram of filament lengths measured for the representative micrograph shown in panel B. The bin size is 3 µm.
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    Image Search Results


    Quantification of filament numbers and lengths. (A) Flow-chart summarizing image processing and quantification steps. (B) Representative micrograph of filaments polymerized from 2 µM actin monomers, labeled with FITC-phalloidin, diluted into microscopy buffer, and imaged using TIRF microscopy. (C-E) Micrograph shown in B following sequential (C) noise filtering and background subtraction, (D) normalization and image thresholding, and (E) binarization and skeletonization. (F) (Left) Following image processing, overlapping filaments are detected as errors and highlighted in red. (Right) Each misidentified filament is processed individually until all errors are resolved, and all filaments are highlighted in green. (G) Histogram of filament lengths measured for the representative micrograph shown in panel B. The bin size is 3 µm.

    Journal: Biology Open

    Article Title: Computational tools for quantifying actin filament numbers, lengths, and bundling

    doi: 10.1242/bio.060267

    Figure Lengend Snippet: Quantification of filament numbers and lengths. (A) Flow-chart summarizing image processing and quantification steps. (B) Representative micrograph of filaments polymerized from 2 µM actin monomers, labeled with FITC-phalloidin, diluted into microscopy buffer, and imaged using TIRF microscopy. (C-E) Micrograph shown in B following sequential (C) noise filtering and background subtraction, (D) normalization and image thresholding, and (E) binarization and skeletonization. (F) (Left) Following image processing, overlapping filaments are detected as errors and highlighted in red. (Right) Each misidentified filament is processed individually until all errors are resolved, and all filaments are highlighted in green. (G) Histogram of filament lengths measured for the representative micrograph shown in panel B. The bin size is 3 µm.

    Article Snippet: A thresholding algorithm (implemented using MATLAB's ‘graythresh’ and ‘imbinarize’ functions ( https://www.mathworks.com/help/images/ref/graythresh.html ; https://www.mathworks.com/help/images/ref/imbinarize.html ) is then applied to convert detected objects from a grayscale into binary images ( ).

    Techniques: Labeling, Microscopy

    Kinetic measurements of filament bundling. Filaments were assembled from 2 µM actin monomers, labeled with FITC-phalloidin, and visualized by TIRF microscopy following the addition of 1.4 µM fascin. (A) Time series of micrographs depicting the progress of filament bundling. The same micrographs are also shown in Figure 4 to demonstrate the effects of image segmentation on the resolution of filaments and bundles. (B) Micrograph collected at time=0 s, before bundling has occurred. Following background subtraction, noise filtering, thresholding and skeletonization, the image has been segmented into a grid. The numbers correspond to the threshold fluorescence value (reported in arbitrary units of fluorescence intensity) above which pixels are considered bundled within each segment of the grid. (C) Automated detection of stretches of bundled (yellow) and single (magenta) filaments in each of the micrographs shown in panel A. (D) The fraction of the filamentous actin that is bundled over time for the reaction shown in panel A.

    Journal: Biology Open

    Article Title: Computational tools for quantifying actin filament numbers, lengths, and bundling

    doi: 10.1242/bio.060267

    Figure Lengend Snippet: Kinetic measurements of filament bundling. Filaments were assembled from 2 µM actin monomers, labeled with FITC-phalloidin, and visualized by TIRF microscopy following the addition of 1.4 µM fascin. (A) Time series of micrographs depicting the progress of filament bundling. The same micrographs are also shown in Figure 4 to demonstrate the effects of image segmentation on the resolution of filaments and bundles. (B) Micrograph collected at time=0 s, before bundling has occurred. Following background subtraction, noise filtering, thresholding and skeletonization, the image has been segmented into a grid. The numbers correspond to the threshold fluorescence value (reported in arbitrary units of fluorescence intensity) above which pixels are considered bundled within each segment of the grid. (C) Automated detection of stretches of bundled (yellow) and single (magenta) filaments in each of the micrographs shown in panel A. (D) The fraction of the filamentous actin that is bundled over time for the reaction shown in panel A.

    Article Snippet: A thresholding algorithm (implemented using MATLAB's ‘graythresh’ and ‘imbinarize’ functions ( https://www.mathworks.com/help/images/ref/graythresh.html ; https://www.mathworks.com/help/images/ref/imbinarize.html ) is then applied to convert detected objects from a grayscale into binary images ( ).

    Techniques: Labeling, Microscopy, Fluorescence