The Analyze Of Medical Signal And Imaging Based On EMD And MP Method

Medical imaging process has become one of fastest developing fields accompanying with the developing of computer, network and medical technology. The doctors can have a better observation of the sick area and get a more accurate diagnosis under the help of medical imaging process technology. But the stricter and higher requirement of the technology makes Fourier Transformation and other less adaptive method unable to get the satisfying result.The EMD method and Hilbert-Huang Transform is a new method developed for analyzing nonlinear and non-stationary data. The key part of the method is the "Empirical Mode Decomposing" with which any complicated data set can be decomposed into a finite and often small number of "Intrinsic Mode Function"(IMF) that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and therefore, highly efficient. Since the decomposition method is based on the local characteristic scale of the data, it is applicable to nonlinear and non-stationary process. Empirical Mode Decomposition (EMD) is a new data analysis method developed by Huang and other scientists. has been widely used for marine seismic detection, biomedical and structural health monitoring and other fields.This paper also used the sparse decomposition method to compress medical images. And this paper proposed two solutions to improve the algorithm. One of them is a polynomial fitting algorithm which uses polynomial to fit closed extreme end of sequences. Then similar value can be obtained from polynomial extreme endpoint of the sequences. And it won't produce big swing in the cubic spline interpolation of the endpoint. We choose high-degree spline interpolation that can thereby improve the precision. Through the contrast between the improved algorithm and the original algorithm, this paper demonstrates that the polynomial fitting algorithm can effectively inhibit endpoint effect.