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2d marker endpoint identification algorithm  (MathWorks Inc)


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    Structured Review

    MathWorks Inc 2d marker endpoint identification algorithm
    <t>2D</t> <t>marker</t> <t>endpoint</t> identification method. (a) A ROI selected on the kV projection image. (b) The Laplacian image of the selected ROI. (c) The orientation direction map (in unit of degree) of the ROI image in (a) after the marker template matching. A marker template (17×17 pixels) at the orientation of zero degree is shown in the right corner of (c). (d) Two marker pixel groups selected based on thresholds on (b), orientation classification on (c) and some other criteria. (e) Finally determined marker endpoints (red).
    2d Marker Endpoint Identification 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/result/2d marker endpoint identification algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    2d marker endpoint identification algorithm - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers"

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    Journal: Physics in medicine and biology

    doi: 10.1088/1361-6560/ab4c0d

    2D marker endpoint identification method. (a) A ROI selected on the kV projection image. (b) The Laplacian image of the selected ROI. (c) The orientation direction map (in unit of degree) of the ROI image in (a) after the marker template matching. A marker template (17×17 pixels) at the orientation of zero degree is shown in the right corner of (c). (d) Two marker pixel groups selected based on thresholds on (b), orientation classification on (c) and some other criteria. (e) Finally determined marker endpoints (red).
    Figure Legend Snippet: 2D marker endpoint identification method. (a) A ROI selected on the kV projection image. (b) The Laplacian image of the selected ROI. (c) The orientation direction map (in unit of degree) of the ROI image in (a) after the marker template matching. A marker template (17×17 pixels) at the orientation of zero degree is shown in the right corner of (c). (d) Two marker pixel groups selected based on thresholds on (b), orientation classification on (c) and some other criteria. (e) Finally determined marker endpoints (red).

    Techniques Used: Marker

    Error distribution of the 2D marker endpoints along (a) u and (b) v directions.
    Figure Legend Snippet: Error distribution of the 2D marker endpoints along (a) u and (b) v directions.

    Techniques Used: Marker

    RMS errors, 3D RMS error and percentage of time for motion errors exceeding 1.0 and 3.0 mm in simulation studies -- I. without noise in the 2D marker positions, II. with different levels of errors in 2D marker positions and III with an image acquisition frequency of every 0.3 sec, and in phantom experiments.
    Figure Legend Snippet: RMS errors, 3D RMS error and percentage of time for motion errors exceeding 1.0 and 3.0 mm in simulation studies -- I. without noise in the 2D marker positions, II. with different levels of errors in 2D marker positions and III with an image acquisition frequency of every 0.3 sec, and in phantom experiments.

    Techniques Used: Marker

    Mean and standard deviation of rotational angle errors in simulation studies -- I. without noise in the 2D marker positions and II. with different levels of errors in 2D marker positions, and in phantom experiments.
    Figure Legend Snippet: Mean and standard deviation of rotational angle errors in simulation studies -- I. without noise in the 2D marker positions and II. with different levels of errors in 2D marker positions, and in phantom experiments.

    Techniques Used: Standard Deviation, Marker

    Top row: marker trajectories reconstructed with the 6-DoF PM3 method and that of the ground truth, along (a1) LR, (a2) AP and (a3) SI directions, in a simulation study of TRM type assuming accurate 2D marker positions. Bottom row: the reconstruction errors along the three directions.
    Figure Legend Snippet: Top row: marker trajectories reconstructed with the 6-DoF PM3 method and that of the ground truth, along (a1) LR, (a2) AP and (a3) SI directions, in a simulation study of TRM type assuming accurate 2D marker positions. Bottom row: the reconstruction errors along the three directions.

    Techniques Used: Marker

    Translation (top row) and rotation angle (bottom row) along LR, AP and SI directions in a representative simulation case with the standard deviation of 2D marker position error being 0.22 mm.
    Figure Legend Snippet: Translation (top row) and rotation angle (bottom row) along LR, AP and SI directions in a representative simulation case with the standard deviation of 2D marker position error being 0.22 mm.

    Techniques Used: Standard Deviation, Marker

    The illustration of the performance of the 2D marker identification algorithm to identify markers with overlap. (a) The kV projection data and (b) the corresponding marker identification results. Points labeled with 1 (yellow) and 2 (blue) are the two markers while the ones labeled with 3 (white) are the overlapped region.
    Figure Legend Snippet: The illustration of the performance of the 2D marker identification algorithm to identify markers with overlap. (a) The kV projection data and (b) the corresponding marker identification results. Points labeled with 1 (yellow) and 2 (blue) are the two markers while the ones labeled with 3 (white) are the overlapped region.

    Techniques Used: Marker, Labeling



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    2d marker endpoint identification algorithm  (MathWorks Inc)
    90
    MathWorks Inc 2d marker endpoint identification algorithm
    <t>2D</t> <t>marker</t> <t>endpoint</t> identification method. (a) A ROI selected on the kV projection image. (b) The Laplacian image of the selected ROI. (c) The orientation direction map (in unit of degree) of the ROI image in (a) after the marker template matching. A marker template (17×17 pixels) at the orientation of zero degree is shown in the right corner of (c). (d) Two marker pixel groups selected based on thresholds on (b), orientation classification on (c) and some other criteria. (e) Finally determined marker endpoints (red).
    2d Marker Endpoint Identification 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/result/2d marker endpoint identification algorithm/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    2d marker endpoint identification algorithm - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    2D marker endpoint identification method. (a) A ROI selected on the kV projection image. (b) The Laplacian image of the selected ROI. (c) The orientation direction map (in unit of degree) of the ROI image in (a) after the marker template matching. A marker template (17×17 pixels) at the orientation of zero degree is shown in the right corner of (c). (d) Two marker pixel groups selected based on thresholds on (b), orientation classification on (c) and some other criteria. (e) Finally determined marker endpoints (red).

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: 2D marker endpoint identification method. (a) A ROI selected on the kV projection image. (b) The Laplacian image of the selected ROI. (c) The orientation direction map (in unit of degree) of the ROI image in (a) after the marker template matching. A marker template (17×17 pixels) at the orientation of zero degree is shown in the right corner of (c). (d) Two marker pixel groups selected based on thresholds on (b), orientation classification on (c) and some other criteria. (e) Finally determined marker endpoints (red).

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Marker

    Error distribution of the 2D marker endpoints along (a) u and (b) v directions.

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: Error distribution of the 2D marker endpoints along (a) u and (b) v directions.

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Marker

    RMS errors, 3D RMS error and percentage of time for motion errors exceeding 1.0 and 3.0 mm in simulation studies -- I. without noise in the 2D marker positions, II. with different levels of errors in 2D marker positions and III with an image acquisition frequency of every 0.3 sec, and in phantom experiments.

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: RMS errors, 3D RMS error and percentage of time for motion errors exceeding 1.0 and 3.0 mm in simulation studies -- I. without noise in the 2D marker positions, II. with different levels of errors in 2D marker positions and III with an image acquisition frequency of every 0.3 sec, and in phantom experiments.

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Marker

    Mean and standard deviation of rotational angle errors in simulation studies -- I. without noise in the 2D marker positions and II. with different levels of errors in 2D marker positions, and in phantom experiments.

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: Mean and standard deviation of rotational angle errors in simulation studies -- I. without noise in the 2D marker positions and II. with different levels of errors in 2D marker positions, and in phantom experiments.

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Standard Deviation, Marker

    Top row: marker trajectories reconstructed with the 6-DoF PM3 method and that of the ground truth, along (a1) LR, (a2) AP and (a3) SI directions, in a simulation study of TRM type assuming accurate 2D marker positions. Bottom row: the reconstruction errors along the three directions.

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: Top row: marker trajectories reconstructed with the 6-DoF PM3 method and that of the ground truth, along (a1) LR, (a2) AP and (a3) SI directions, in a simulation study of TRM type assuming accurate 2D marker positions. Bottom row: the reconstruction errors along the three directions.

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Marker

    Translation (top row) and rotation angle (bottom row) along LR, AP and SI directions in a representative simulation case with the standard deviation of 2D marker position error being 0.22 mm.

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: Translation (top row) and rotation angle (bottom row) along LR, AP and SI directions in a representative simulation case with the standard deviation of 2D marker position error being 0.22 mm.

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Standard Deviation, Marker

    The illustration of the performance of the 2D marker identification algorithm to identify markers with overlap. (a) The kV projection data and (b) the corresponding marker identification results. Points labeled with 1 (yellow) and 2 (blue) are the two markers while the ones labeled with 3 (white) are the overlapped region.

    Journal: Physics in medicine and biology

    Article Title: A method to reconstruct intra-fractional liver motion in rotational radiotherapy using linear fiducial markers

    doi: 10.1088/1361-6560/ab4c0d

    Figure Lengend Snippet: The illustration of the performance of the 2D marker identification algorithm to identify markers with overlap. (a) The kV projection data and (b) the corresponding marker identification results. Points labeled with 1 (yellow) and 2 (blue) are the two markers while the ones labeled with 3 (white) are the overlapped region.

    Article Snippet: In our current version (Matlab based), the computational time for the 2D marker endpoint identification algorithm to identify four marker endpoints from one projection was ~0.31 sec.

    Techniques: Marker, Labeling