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2d linear interpolation interp2.m  (MathWorks Inc)


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    Structured Review

    MathWorks Inc 2d linear interpolation interp2.m
    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by <t>2D</t> spatial <t>interpolation</t> (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.
    2d Linear Interpolation Interp2.M, 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/2d+linear+interpolation+interp2%2Em/pmc05798010-101-11-18
    Average 90 stars, based on 1 article reviews
    2d linear interpolation interp2.m - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Improved Super-Resolution Ultrasound Microvessel Imaging with Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking"

    Article Title: Improved Super-Resolution Ultrasound Microvessel Imaging with Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    doi: 10.1109/TUFFC.2017.2778941

    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by 2D spatial interpolation (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.
    Figure Legend Snippet: Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by 2D spatial interpolation (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.

    Techniques Used: Derivative Assay

    Related Articles

    Derivative Assay:

    Article Title: Improved Super-Resolution Ultrasound Microvessel Imaging with Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking
    Article Snippet: to this specific dataset) of the maximum microbubble intensity value in each frame) to further suppress unreliable microbubble signal and noise. .. The data was then spatially interpolated to 0.02 mm resolution using 2D linear interpolation (the “interp2.m” function in Matlab), as shown in . .. A system PSF ( ) was derived based on typically observed microbubble signal dimensions and shape (i.e., full width at half maximum, ) using a multivar



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    2d linear interpolation interp2.m  (MathWorks Inc)
    90
    MathWorks Inc 2d linear interpolation interp2.m
    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by <t>2D</t> spatial <t>interpolation</t> (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.
    2d Linear Interpolation Interp2.M, 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/2d+linear+interpolation+interp2%2Em/pmc05798010-101-11-18
    Average 90 stars, based on 1 article reviews
    2d linear interpolation interp2.m - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by 2D spatial interpolation (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.

    Journal: IEEE transactions on ultrasonics, ferroelectrics, and frequency control

    Article Title: Improved Super-Resolution Ultrasound Microvessel Imaging with Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking

    doi: 10.1109/TUFFC.2017.2778941

    Figure Lengend Snippet: Processes of microbubble localization. (a) shows an example of a local region of the microbubble data, followed by 2D spatial interpolation (b), normalized 2D cross-correlation with the derived system PSF (c), cross-correlation coefficient thresholding (d), and finally identifying microbubble center locations by searching local maximum correlation coefficient. (e) shows an example of the localization indicated by the red crosses. (f) shows a zoomed-in image of a microbubble signal and (g) shows the derived PSF signal based on (f). (h) shows one example of the microbubble localization result on the full FOV data.

    Article Snippet: The data was then spatially interpolated to 0.02 mm resolution using 2D linear interpolation (the “interp2.m” function in Matlab), as shown in .

    Techniques: Derivative Assay