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2d correlation coefficient  (MathWorks Inc)


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    MathWorks Inc 2d correlation coefficient
    2d Correlation Coefficient, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2900 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/correlation+coefficient+function/Image+Processing+Toolbox/pmc05807854-336-17-29
    Average 96 stars, based on 2900 article reviews
    2d correlation coefficient - by Bioz Stars, 2026-09
    96/100 stars

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    Article Snippet: MATLAB with Image processing toolbox and Statistics and Machine learning toolbox , Mathworks , https://www.mathworks.com/.

    Article Title: Blocking microglial reactivity via purinergic receptors prevents subacute cognitive deficits after TIA.
    Article Snippet: For the microglia morphology analysis, confocal Z-stack images were processed, and microglial morphology features were extracted using custom-written scripts in MATLAB (R216b, The MathWorks, Natick, Massachusetts, USA), with dependencies on the Image Processing Toolbox as well as Statistics and Machine Learning Toolbox.

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    Article Snippet: All data analysis is performed using MATLAB R2020a (The MathWorks, Inc., Natick, MA, USA) scripts, including the Image Processing Toolbox and functions of the Nanodiffraction Toolbox published by Nicolas et al. (2017).

    Article Title: Association of Maternal Antenatal Distress with Child Amygdala-Prefrontal Cortex Functional Connectivity at 2 - 3 Years in a South African Birth Cohort Study.
    Article Snippet: PFC parcellated areas were grouped according to their anatomical locations into four larger areas as the regions of interest (ROIs) (Figure 2) using the MATLAB (R2023b) image processing toolbox (version 23.2) [48].

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    Article Snippet: Image processing in MATLAB We use MATLAB’s image processing toolbox to analyse experimental images for the hyphal tracking, area coverage, and biofilm front light-sheet analyses presented in this work.

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    Article Title: System and method for improving the clarity of overlapping objects
    Article Snippet: .. Such image processing software includes, but is not limited to, YOLO (real-time object detection algorithm that can identify objects and draw bounding boxes in a single pass through the image), Faster R-CNN (A deep learning-based framework that detects objects by proposing regions of interest and refining their boundaries), Single Shot MultiBox Detector (Detects objects and their bounding boxes using a single forward pass of a convolutional neural network), OpenCV (An open-source computer vision library that includes functions for image processing and drawing bounding boxes, though typically combined with machine learning models for detection), and MATLAB Image Processing Toolbox (tools for detecting and marking objects or text regions with bounding boxes, suitable for academic and research purposes). ..

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    Article Title: System and method for improving the clarity of overlapping objects
    Article Snippet: .. Such image processing software includes, but is not limited to, YOLO (real-time object detection algorithm that can identify objects and draw bounding boxes in a single pass through the image), Faster R-CNN (A deep learning-based framework that detects objects by proposing regions of interest and refining their boundaries), Single Shot MultiBox Detector (Detects objects and their bounding boxes using a single forward pass of a convolutional neural network), OpenCV (An open-source computer vision library that includes functions for image processing and drawing bounding boxes, though typically combined with machine learning models for detection), and MATLAB Image Processing Toolbox (tools for detecting and marking objects or text regions with bounding boxes, suitable for academic and research purposes). ..



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    (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, <t>Pearson</t> cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.
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    Image Search Results


    (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, Pearson cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.

    Journal: bioRxiv

    Article Title: Large-scale deep tissue voltage imaging with targeted illumination confocal microscopy

    doi: 10.1101/2023.07.21.548930

    Figure Lengend Snippet: (a-c) Example Voltron2 fluorescence images under targeted illumination with confocal slit width set to 4.5, 22.5, and 156 µm. Scale bar 50 µm. (d) Voltron2 fluorescence image over the same FOV but acquired without targeted illumination and with a confocal slit width of 156 µm. TI, targeted illumination. Scale bar 50 µm. (e,h,i) Comparison of spike Δ F / F , spike detection fidelity d ′ , and spike SNR measured with targeted illumination and confocal slit widths of 4.5, 11.3, 22.5, and 156 µm (n = 30 cells from 6 FOVs, 2 mice). Box plots: box, 25th (Q1, bottom line) to 75 th (Q3, top line) percentiles; whiskers, Q 1 − 1.5 × I Q R to Q 3 + 1.5 × I Q R , where I Q R = Q 3 − Q 1 ; middle line, median (m); notch, from m − 1.57 × I Q R / n to m + 1.57 × I Q R / n ; dots, measurement points. p < 0.05, p < 0.01, p < 0.001, no label if p ≥ 0.05, pairwise Wilcoxon signed-rank test, see for statistics. (f,g,j) Comparison of spike Δ F / F , photobleaching rate, and spike SNR measured with and without targeted illumination when using a 14 µm confocal slit. For (f,j), n = 19 cells from 5 FOVs, 2 mice. For (g), n = 92 cells from 5 FOVs, 2 mice. (l,m,n) Example images (scale bar, 20 µm) and corresponding fluorescence traces from two neighboring neurons with targeted illumination and confocal slit widths of 4.5, 22.5, and 156 µm (from top to bottom). Gray line, fluorescence traces; red line, extracted subthreshold Vm traces; r, Pearson cross-correlation coefficient between the subthreshold Vm traces from the 2 neurons.

    Article Snippet: To analyze Vm-Vm correlations, we calculated Pearson cross-correlation coefficients (Matlab function corrcoef ) for the extracted subthreshold traces F s u b t from pairs of neurons.

    Techniques: Fluorescence, Comparison, IF-P