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Proceedings Paper

A generalized method for computation of n-dimensional Radon transforms
Author(s): Robert Frysch; Tim Pfeiffer; Georg Rose
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Paper Abstract

Radon transforms allow to represent n-dimensional objects by all their possible (n-1)-dimensional integrals. They find broad usage in a variety of image processing topics, covering pattern recognition and tomographic imaging. Potentially the most frequently used version is the 2D Radon transform which is commonly computed by means of the ray tracing procedure (Siddon, Joseph etc.).1 The problem comes down to a method of approximating a line integral through a 2D pixelized image. For higher dimensions, however, this problem becomes more and more complex and typically involves a substantial amount of case differentiation, making it particularly ill-suited for use in massively parallelized computation (e.g. on GPUs). Additionally, implementation effort is substantial and quite error-prone. Here, we propose a simple strategy to compute the (n-1)-dimensional integrals in a generalized manner by reducing the sampling problem to a single matrix multiplication. We further present OpenCL implementations for n=2 and n=3, making use of hardware interpolation methods on texture memory of GPU devices to provide a fast computation of the transform.

Paper Details

Date Published: 10 March 2020
PDF: 7 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132G (10 March 2020); doi: 10.1117/12.2549586
Show Author Affiliations
Robert Frysch, Univ. of Magdeburg (Germany)
Tim Pfeiffer, Univ. of Magdeburg (Germany)
Georg Rose, Univ. of Magdeburg (Germany)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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