Study on Medical Image Processing Algorithm Based on Contourlet Transform and Correlation Theory

In recent years, processed medical image becomes more and more important to diagnosis. Anamorphic image may result in wrong judgment on state of an illness, even leads to fateful danger. Meanwhile, more efficient data transportation and storage are required with the development of high-resolution photography. Therefore, thousands of researchers work on the field of medical image processing. In this paper, contourlet transform, which may provide with tight bracing and multi-scale analysis, is introduced. And a new medical image processing algorithm based on contourlet transform and correlation theory is presented. This algorithm possesses some excellent performances such as multi-scale analysis, time-frequency-localization and multi-directions. Especially, this algorithm has excellent performance when describing anisotropic 2-D data. So high-quality medical image can be reconstructed even though a relative few coefficients are employed.To verify the algorithm, some medical images were processed. The experimental results show that this algorithm has better performance in compression and denoising than that of wavelet transform.