This paper compares various interpolation methods used in medical image processing, including truncated and windowed sinc, nearest neighbor, linear, quadratic, cubic B-spline, cubic, Lagrange, and Gaussian interpolation. The study evaluates these methods based on spatial and Fourier analyses, computational complexity, runtime, and interpolation error for common medical imaging tasks. The goal is to identify the most suitable interpolation methods for medical applications. The paper introduces a standardized notation for analyzing interpolators and derives fundamental properties. Successful methods are those that are DC-constant and interpolators rather than DC-inconstant or approximators. Three novel kernels are introduced: the 6×6 Blackman–Harris windowed sinc interpolator and C2-continuous cubic kernels with N = 6 and N = 8 supporting points. Quantitative error evaluations were performed using 50 digital X-ray images. Large kernel sizes were found to be superior to small ones, with N = 6 or larger kernels performing significantly better than N = 2 or N = 3 point methods. The cubic 6×6 interpolator with continuous second derivatives is recommended for most common interpolation tasks due to its excellent local and Fourier properties, ease of implementation, and minimal errors. It is also faster than other six-point kernels and avoids the border effects of B-spline interpolation. The study does not aim to determine a single best method but to provide a comprehensive catalog of methods in a uniform terminology, define general properties and requirements of local techniques, and enable the reader to select the optimal method for specific medical imaging applications.This paper compares various interpolation methods used in medical image processing, including truncated and windowed sinc, nearest neighbor, linear, quadratic, cubic B-spline, cubic, Lagrange, and Gaussian interpolation. The study evaluates these methods based on spatial and Fourier analyses, computational complexity, runtime, and interpolation error for common medical imaging tasks. The goal is to identify the most suitable interpolation methods for medical applications. The paper introduces a standardized notation for analyzing interpolators and derives fundamental properties. Successful methods are those that are DC-constant and interpolators rather than DC-inconstant or approximators. Three novel kernels are introduced: the 6×6 Blackman–Harris windowed sinc interpolator and C2-continuous cubic kernels with N = 6 and N = 8 supporting points. Quantitative error evaluations were performed using 50 digital X-ray images. Large kernel sizes were found to be superior to small ones, with N = 6 or larger kernels performing significantly better than N = 2 or N = 3 point methods. The cubic 6×6 interpolator with continuous second derivatives is recommended for most common interpolation tasks due to its excellent local and Fourier properties, ease of implementation, and minimal errors. It is also faster than other six-point kernels and avoids the border effects of B-spline interpolation. The study does not aim to determine a single best method but to provide a comprehensive catalog of methods in a uniform terminology, define general properties and requirements of local techniques, and enable the reader to select the optimal method for specific medical imaging applications.