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3 d pair  (MathWorks Inc)


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    MathWorks Inc 3 d pair
    Example of sparse image representation using SLIC: (a) coronal view <t>of</t> <t>3-D</t> CT lung and liver volume, (b) projection through 3-D supervoxel representation with supervoxel boundaries and (c) with assignment of mean intensity. The SLIC algorithm with different values of the parameter K = 11,000 (top) and K = 5500 (bottom) shows that clustering is consistent in image regions with sufficient structural information (close to edges, e.g., the sliding surfaces of lungs), while different clusters are generated in homogeneous image regions.
    3 D Pair, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 310 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/3 d pair/product/MathWorks Inc
    Average 96 stars, based on 310 article reviews
    3 d pair - by Bioz Stars, 2026-05
    96/100 stars

    Images

    1) Product Images from "GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications"

    Article Title: GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

    Journal: Journal of Medical Imaging

    doi: 10.1117/1.JMI.5.2.024001

    Example of sparse image representation using SLIC: (a) coronal view of 3-D CT lung and liver volume, (b) projection through 3-D supervoxel representation with supervoxel boundaries and (c) with assignment of mean intensity. The SLIC algorithm with different values of the parameter K = 11,000 (top) and K = 5500 (bottom) shows that clustering is consistent in image regions with sufficient structural information (close to edges, e.g., the sliding surfaces of lungs), while different clusters are generated in homogeneous image regions.
    Figure Legend Snippet: Example of sparse image representation using SLIC: (a) coronal view of 3-D CT lung and liver volume, (b) projection through 3-D supervoxel representation with supervoxel boundaries and (c) with assignment of mean intensity. The SLIC algorithm with different values of the parameter K = 11,000 (top) and K = 5500 (bottom) shows that clustering is consistent in image regions with sufficient structural information (close to edges, e.g., the sliding surfaces of lungs), while different clusters are generated in homogeneous image regions.

    Techniques Used: Generated

    Main anatomical views of 3-D CT registration results for case #P0 of the liver dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded (red-cyan) intensity differences between volume pair before registration (left), after registration using Demons with isotropic Gaussian kernel, iso-dem, (middle), and guided image filtering with random SLIC clustering, rdn-gif, (right). Registration using our method (right) improves registration accuracy especially close to the lung and liver surfaces (depicted by corresponding red dotted and green solid arrows, respectively).
    Figure Legend Snippet: Main anatomical views of 3-D CT registration results for case #P0 of the liver dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded (red-cyan) intensity differences between volume pair before registration (left), after registration using Demons with isotropic Gaussian kernel, iso-dem, (middle), and guided image filtering with random SLIC clustering, rdn-gif, (right). Registration using our method (right) improves registration accuracy especially close to the lung and liver surfaces (depicted by corresponding red dotted and green solid arrows, respectively).

    Techniques Used:

    Main anatomical views of resulting 3-D displacement fields for case #P0 of the liver CT dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded magnitude of the displacement field estimated using Demons with isotropic Gaussian kernel, iso-dem, (middle) and guided image filtering with random SLIC clustering, rdn-gif, (right). (left) The reference image with the corresponding blue contour is shown for a guidance to the displacement field. Registration using our method (right) produces a visually smooth displacement field inside the lungs and liver, and at the same, estimates sliding motion at the lung and liver interface [depicted by corresponding red dotted (for lungs) and green solid (for liver) arrows].
    Figure Legend Snippet: Main anatomical views of resulting 3-D displacement fields for case #P0 of the liver CT dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded magnitude of the displacement field estimated using Demons with isotropic Gaussian kernel, iso-dem, (middle) and guided image filtering with random SLIC clustering, rdn-gif, (right). (left) The reference image with the corresponding blue contour is shown for a guidance to the displacement field. Registration using our method (right) produces a visually smooth displacement field inside the lungs and liver, and at the same, estimates sliding motion at the lung and liver interface [depicted by corresponding red dotted (for lungs) and green solid (for liver) arrows].

    Techniques Used:



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    Example of sparse image representation using SLIC: (a) coronal view <t>of</t> <t>3-D</t> CT lung and liver volume, (b) projection through 3-D supervoxel representation with supervoxel boundaries and (c) with assignment of mean intensity. The SLIC algorithm with different values of the parameter K = 11,000 (top) and K = 5500 (bottom) shows that clustering is consistent in image regions with sufficient structural information (close to edges, e.g., the sliding surfaces of lungs), while different clusters are generated in homogeneous image regions.
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    Image Search Results


    Example of sparse image representation using SLIC: (a) coronal view of 3-D CT lung and liver volume, (b) projection through 3-D supervoxel representation with supervoxel boundaries and (c) with assignment of mean intensity. The SLIC algorithm with different values of the parameter K = 11,000 (top) and K = 5500 (bottom) shows that clustering is consistent in image regions with sufficient structural information (close to edges, e.g., the sliding surfaces of lungs), while different clusters are generated in homogeneous image regions.

    Journal: Journal of Medical Imaging

    Article Title: GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

    doi: 10.1117/1.JMI.5.2.024001

    Figure Lengend Snippet: Example of sparse image representation using SLIC: (a) coronal view of 3-D CT lung and liver volume, (b) projection through 3-D supervoxel representation with supervoxel boundaries and (c) with assignment of mean intensity. The SLIC algorithm with different values of the parameter K = 11,000 (top) and K = 5500 (bottom) shows that clustering is consistent in image regions with sufficient structural information (close to edges, e.g., the sliding surfaces of lungs), while different clusters are generated in homogeneous image regions.

    Article Snippet: The computation time per registration using the presented framework is ≈ 3 min per 3-D pair (on a standard CPU, running nonoptimized C++ code, MATLAB™ mex compiler) and is several times faster than our previous bilateral filtering procedure ( ≈ 60 min ) or the locally adaptive anisotropic regularization (several hours).

    Techniques: Generated

    Main anatomical views of 3-D CT registration results for case #P0 of the liver dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded (red-cyan) intensity differences between volume pair before registration (left), after registration using Demons with isotropic Gaussian kernel, iso-dem, (middle), and guided image filtering with random SLIC clustering, rdn-gif, (right). Registration using our method (right) improves registration accuracy especially close to the lung and liver surfaces (depicted by corresponding red dotted and green solid arrows, respectively).

    Journal: Journal of Medical Imaging

    Article Title: GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

    doi: 10.1117/1.JMI.5.2.024001

    Figure Lengend Snippet: Main anatomical views of 3-D CT registration results for case #P0 of the liver dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded (red-cyan) intensity differences between volume pair before registration (left), after registration using Demons with isotropic Gaussian kernel, iso-dem, (middle), and guided image filtering with random SLIC clustering, rdn-gif, (right). Registration using our method (right) improves registration accuracy especially close to the lung and liver surfaces (depicted by corresponding red dotted and green solid arrows, respectively).

    Article Snippet: The computation time per registration using the presented framework is ≈ 3 min per 3-D pair (on a standard CPU, running nonoptimized C++ code, MATLAB™ mex compiler) and is several times faster than our previous bilateral filtering procedure ( ≈ 60 min ) or the locally adaptive anisotropic regularization (several hours).

    Techniques:

    Main anatomical views of resulting 3-D displacement fields for case #P0 of the liver CT dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded magnitude of the displacement field estimated using Demons with isotropic Gaussian kernel, iso-dem, (middle) and guided image filtering with random SLIC clustering, rdn-gif, (right). (left) The reference image with the corresponding blue contour is shown for a guidance to the displacement field. Registration using our method (right) produces a visually smooth displacement field inside the lungs and liver, and at the same, estimates sliding motion at the lung and liver interface [depicted by corresponding red dotted (for lungs) and green solid (for liver) arrows].

    Journal: Journal of Medical Imaging

    Article Title: GIFTed Demons: deformable image registration with local structure-preserving regularization using supervoxels for liver applications

    doi: 10.1117/1.JMI.5.2.024001

    Figure Lengend Snippet: Main anatomical views of resulting 3-D displacement fields for case #P0 of the liver CT dataset: (a) coronal, (b) axial, and (c) sagittal views for the color-coded magnitude of the displacement field estimated using Demons with isotropic Gaussian kernel, iso-dem, (middle) and guided image filtering with random SLIC clustering, rdn-gif, (right). (left) The reference image with the corresponding blue contour is shown for a guidance to the displacement field. Registration using our method (right) produces a visually smooth displacement field inside the lungs and liver, and at the same, estimates sliding motion at the lung and liver interface [depicted by corresponding red dotted (for lungs) and green solid (for liver) arrows].

    Article Snippet: The computation time per registration using the presented framework is ≈ 3 min per 3-D pair (on a standard CPU, running nonoptimized C++ code, MATLAB™ mex compiler) and is several times faster than our previous bilateral filtering procedure ( ≈ 60 min ) or the locally adaptive anisotropic regularization (several hours).

    Techniques: