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Achieving enhanced accuracy and strength performance with parallel programming for invariant affine point cloud registration

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Abstract

Affine-transform of tomographic images maps pixels from image to world coordinates. However, affine transform application on each pixel consumes much time. Extraction of the point cloud of interest from the background is another challenge. The benchmark algorithms use approximations, therefore, compromising accuracy. Because of this fact, there arises a need for affine registration for 3D reconstruction. In this work, we present a computationally efficient affine registration of Digital Imaging and COmmunications in Medicine (DICOM) images. We introduce a novel GPU accelerated hierarchical clustering algorithm using Gaussian thresholding of inter-coordinate distances followed by maximal mutual information score merging for clutter removal. We also show that the reconstructed 3d models using our methodology have a best-case minimum error of 0.18 cm against physical measurements and have higher structural strength. This algorithm should apply to reconstruction, 3D printing, virtual reality, and 3D visualization.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, SS CASE IT, Street 33, Block A, Multi Garden, Sector B-17, Islamabad, Pakistan

    Usman Khan

  2. Air University, PAF Complex E9, Islamabad, Pakistan

    Amanullah Yasin & Ahmed Jalal

  3. Interdisciplinary Research Center, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Muhammad Abid

  4. Department of Mechanical Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan

    Muhammad Abid

Authors
  1. Usman Khan
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  2. Amanullah Yasin
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  3. Ahmed Jalal
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  4. Muhammad Abid
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Correspondence to Usman Khan.

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Khan, U., Yasin, A., Jalal, A. et al. Achieving enhanced accuracy and strength performance with parallel programming for invariant affine point cloud registration. Multimed Tools Appl 82, 2587–2615 (2023). https://doi.org/10.1007/s11042-022-13178-3

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  • DOI: https://doi.org/10.1007/s11042-022-13178-3

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