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matlab's regionprops algorithm  (MathWorks Inc)


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    MathWorks Inc matlab's regionprops algorithm
    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm <t>regionprops</t> in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)
    Matlab's Regionprops Algorithm, 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+regionprops+algorithm/pmc10276800-232-90-89
    Average 90 stars, based on 1 article reviews
    matlab's regionprops algorithm - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Waves in a Stochastic Cell Motility Model"

    Article Title: Waves in a Stochastic Cell Motility Model

    Journal: Bulletin of Mathematical Biology

    doi: 10.1007/s11538-023-01164-1

    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm regionprops in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)
    Figure Legend Snippet: In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm regionprops in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)

    Techniques Used: Activation Assay, Standard Deviation

    Related Articles

    Activation Assay:

    Article Title: Waves in a Stochastic Cell Motility Model
    Article Snippet: .. For Figs. and , MATLAB’s regionprops algorithm is used to identify the activation events automatically. ..

    Article Title: Waves in a Stochastic Cell Motility Model.
    Article Snippet: .. A.2 Pattern Recognition For Figs. 8 and 9, MATLAB’s regionprops algorithm is used to identify the activation events automatically. ..

    Article Title: Waves in a Stochastic Cell Motility Model
    Article Snippet: .. In ( a ), we only show the simulation of wave integrated up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=20$$\end{document} T = 20 because the solution remains in the background state; afterwards, the other three figures are shown up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=100$$\end{document} T = 100 (Color figure online) In order to quantify this notion of optimality in the noise intensity, we must first quantify the size and shape of the patterns in Fig. b and c. Using MATLAB’s regionprops algorithm, we can automatically detect the patches with a high value for the activator u (see “Appendix A” for details), giving us the possibility to compute the number of activation events and determine the width and duration of each event, see Fig. a. ..

    Article Title: Waves in a Stochastic Cell Motility Model.
    Article Snippet: .. In order to quantify this notion of optimality in the noise intensity, we must first quantify the size and shape of the patterns in Fig. 7b and c. Using MATLAB’s regionprops algorithm, we can automatically detect the patches with a high value for the activator u (see “Appendix A” for details), giving us the possibility to compute the number of activation events and determine the width and duration of each event, see Fig. 8a. ..

    Migration:

    Article Title: Oscillatory Strain Promotes Vessel Stabilization and Alignment through Fibroblast YAP‐Mediated Mechanosensitivity
    Article Snippet: Cells, nuclei, vessels, and collagen IV orientation were determined using the orientation module in the regionprops function in MATLAB software. .. Cell migration pattern, VE‐cadherin morphogenesis, and vessel quality under blebbistatin treatment were measured by the eccentricity parameter using regionprops algorithm in the MATLAB software. ..

    other:

    Article Title: Improving Transcranial Acoustic Targeting: The Limits of CT-Based Velocity Estimates and the Role of MR
    Article Snippet: Pore eccentricity was measured in two dimensions using MATLAB’s regionprops algorithm.



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    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm <t>regionprops</t> in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)
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    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm <t>regionprops</t> in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)
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    In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm regionprops in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)

    Journal: Bulletin of Mathematical Biology

    Article Title: Waves in a Stochastic Cell Motility Model

    doi: 10.1007/s11538-023-01164-1

    Figure Lengend Snippet: In ( a ), we show a simulation similar to those in Fig. , but with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =0.045$$\end{document} σ = 0.045 and a homogeneous initial condition with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(u^*,4v^*)$$\end{document} ( u ∗ , 4 v ∗ ) plus a small perturbation. The red boxes are the result of the pattern finding algorithm regionprops in MATLAB; it identifies all the regions of excitations which we would also find by eye, see “Appendix A” for details. In ( b ), we used this algorithm to find the length, width and maximum of these pulses (left axis), as well as the total number of activation events (right axis). For each value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma $$\end{document} σ , the number of events is averaged over 100 simulations, and the length, width and maximum are averaged over all events in the 100 simulations. We plot the average together with the standard deviation (Color figure online)

    Article Snippet: In ( a ), we only show the simulation of wave integrated up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=20$$\end{document} T = 20 because the solution remains in the background state; afterwards, the other three figures are shown up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T=100$$\end{document} T = 100 (Color figure online) In order to quantify this notion of optimality in the noise intensity, we must first quantify the size and shape of the patterns in Fig. b and c. Using MATLAB’s regionprops algorithm, we can automatically detect the patches with a high value for the activator u (see “Appendix A” for details), giving us the possibility to compute the number of activation events and determine the width and duration of each event, see Fig. a.

    Techniques: Activation Assay, Standard Deviation