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


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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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 (2022).

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  • Cuda
  • Point cloud
  • Reconstruction
  • Tomography
  • Virtual reality