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Survey: interpolation methods in medical image processing.

T M Lehmann1, C Gönner, K Spitzer

  • 1Institute of Medical Informatics, Aachen University of Technology (RWTH), Germany. lehmann@computer.org

IEEE Transactions on Medical Imaging
|February 8, 2000
PubMed
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This study compares various medical image interpolation techniques, finding that larger kernel sizes generally perform better. The 6x6 cubic interpolator is recommended for its speed, accuracy, and ease of implementation in medical imaging tasks.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Image interpolation is crucial for medical imaging tasks like generation, compression, and resampling.
  • Finite-sized interpolation kernels are used as approximations for the ideal, unlimited sinc function.
  • Various interpolation methods exist, each with unique properties and performance characteristics.

Purpose of the Study:

  • To comprehensively compare different interpolation kernels for medical imaging.
  • To analyze spatial and Fourier properties, computational complexity, and interpolation errors.
  • To identify optimal interpolation methods for common medical imaging applications.

Main Methods:

  • Evaluated kernels: truncated/windowed sinc, nearest neighbor, linear, quadratic, cubic B-spline, cubic, Lagrange, and Gaussian.

Related Experiment Videos

  • Kernel sizes ranged from 1x1 to 8x8.
  • Analyses included spatial/Fourier domain, computational complexity, runtime, and quantitative error determination using 50 digital X-rays.

    • Main Results:

    • Larger kernel sizes generally outperformed smaller ones.
    • Kernels with N=6 or larger significantly improved results compared to N=2 or N=3 (p << 0.005), except for truncated sinc.
    • The 6x6 cubic interpolator with continuous second derivatives and C2-continuous cubic kernels (N=6, N=8) were identified as superior options.

    Conclusions:

    • The 6x6 cubic interpolator is recommended for most medical imaging interpolation tasks due to its speed, accuracy, and favorable properties.
    • It offers advantages over B-spline interpolation by avoiding border effects.
    • The study provides a framework for selecting optimal interpolation methods based on specific application needs.