Research on Interpolation Methods in Medical Image Processing
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Image interpolation is widely used for the field of medical image processing. In this paper, interpolation methods are divided into three groups: filter interpolation, ordinary interpolation and general partial volume interpolation. Some commonly-used filter methods for image interpolation are pioneered, but the interpolation effects need to be further improved. When analyzing and discussing ordinary interpolation, many asymmetrical kernel interpolation methods are proposed. Compared with symmetrical kernel ones, the former are have some advantages. After analyzing the partial volume and generalized partial volume estimation interpolations, the new concept and constraint conditions of the general partial volume interpolation are defined, and several new partial volume interpolation functions are derived. By performing the experiments of image scaling, rotation and self-registration, the interpolation methods mentioned in this paper are compared in the entropy, peak signal-to-noise ratio, cross entropy, normalized cross-correlation coefficient and running time. Among the filter interpolation methods, the median and B-spline filter interpolations have a relatively better interpolating performance. Among the ordinary interpolation methods, on the whole, the symmetrical cubic kernel interpolations demonstrate a strong advantage, especially the symmetrical cubic B-spline interpolation. However, we have to mention that they are very time-consuming and have lower time efficiency. As for the general partial volume interpolation methods, from the total error of image self-registration, the symmetrical interpolations provide certain superiority; but considering the processing efficiency, the asymmetrical interpolations are better.
KeywordsInterpolation Medical image Image scaling Image rotation Image registration
This work is supported by Outstanding Young Scientific Research Fund of Hunan Provincial Education Department, P.R.China (No.09B071) and supported by the Foundation of 11th Five-year Plan for Key Construction Academic Subject (Optics) of Hunan Province, P.R.China.
- 22.Chang, S. G., Cvetkovic, Z., Vetterli, M., Locally adaptive wavelet-based image interpolation. IEEE Trans. Image Process. 15(6), 2006.Google Scholar
- 29.Collignon, A., Maes, F., Delaere, D., et al., Automated multimodality image registration using information theory, in proc. Int. Conf. Information Processing in Medical Imaging: Computational Imaging and Vision, Vol.3, pp. 263–274, 1995.Google Scholar