Wavelet-based Interpolation Scheme for Resolution Enhancement of Medical Images
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A novel interpolation method for resolution enhancement is proposed in this paper. This method estimates wavelet coefficients in the high frequency subbands from a low resolution image using our proposed filters. An inverse wavelet transform is then performed for synthesis of a higher resolution image. Experimental results show that our proposed method outperforms other commonly used schemes objectively and subjectively. In addition, the processing time required in an algorithm-implemented Digital Signal Processor (DSP) is satisfied. By using the DSP hardware platform, off-line interpolation processing becomes easier and more feasible. The interpolated image has merits of high contrast and remarkable sharpness which essentially meet the requirements for interpolation of medical images. Our method can provide better quality of interpolated medical images compared to other methods to assist physicians in making diagnoses.
KeywordsResolution enhancement Interpolation Discrete wavelet transform DSP Medical image
We appreciate the assistance and support of the radiologists from department of radiology, Tzu Chi general hospital, Hualien, Taiwan.
- 1.Pratt, W. K. (1978). Digital image processing. Toronto: Wiley.Google Scholar
- 4.Chang, S. G., Cvetkovic, Z., & Vetterli, M. (1995). Resolution enhancement of image using wavelet transform extrema extrapolation. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, 4, 2379–2382.Google Scholar
- 6.Muresan, D. D., & Parks, T. W. (2000). Prediction of image detail. IEEE International Conference on Image Processing, 2, 323–326.Google Scholar
- 11.Fahmy, G., Black Jr., J., & Panhanathan, S. (2006). Texture characterization for joint compression and classification based on human perception in the wavelet domain. IEEE Transactions on Medical Imaging, 15(6), 1389–1396.Google Scholar
- 15.Bovik, A. (ed.) (2000). Hand book of image and video processing. Academic.Google Scholar
- 16.Prasad, M. N., Sowmya, A., & Hock, I. (2004). Feature subset selection using ICA for classifying emphysema in HRCT images. Proceeding of International Conference on Pattern Recognition, 4, 515–518.Google Scholar
- 17.Prasad, M. N., & Sowmya, A. (2004). Detection of bronchovascular pairs on HRCT lung images through relational learning. International Symposium on Biomedical Imaging, 2, 1135–1138.Google Scholar