Premium
Subunity coordinate translation with Fourier transform to achieve efficient and quality three‐dimensional medical image interpolation
Author(s) -
Chen QinSheng,
Crownover Richard,
Weinhous Martin S.
Publication year - 1999
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598681
Subject(s) - interpolation (computer graphics) , translation (biology) , fourier transform , stairstep interpolation , phase correlation , transformation (genetics) , mathematics , linear interpolation , sinc function , algorithm , image scaling , computer vision , computer science , spline interpolation , artificial intelligence , mathematical analysis , bilinear interpolation , fourier analysis , image processing , image (mathematics) , short time fourier transform , pattern recognition (psychology) , biochemistry , chemistry , messenger rna , gene
A new approach to the interpolation of three‐dimensional (3D) medical images is presented. Instead of going through the conventional interpolation scheme where the continuous function is first reconstructed from the discrete data set and then resampled, the interpolation is achieved with a subunity coordinate translation technique. The original image is first transformed into the spatial‐frequency domain. The phase of the transform is then modified with n − 1 linear phase terms in the axial direction to achieve n − 1 subunity coordinate translations with a distance 1 / n , where n is an interpolation ratio, following the phase shift theorem of Fourier transformation. All the translated images after inverse Fourier transformation are then interspersed in turn into the original image. Since windowing plays an important role in the process, different window functions have been studied and a proper recommendation is provided. The interpolation quality produced with the present method is as good as that with the sampling (sinc) function, while the efficiency, thanks to the fast Fourier transformation, is very much improved. The approach has been validated with both computed tomography (CT) and magnetic resonance (MR) images. The interpolations of 3D CT and MR images are demonstrated.