Medical Image Fusion Based On Bi-dimensional Empirical Model Decomposition

Image fusion is a comprehensive data processing, which used two or more images that are obtained from acquisition devices. to combine information from multiple images more clear, the amount of information larger, higher quality of fusion images. Medical image technology is a modern medical treatment method, which utilized all kinds of imaging devices to observe the internal organization, bone structure and functional change of the human body. Different medical imaging devices provide different medical information, and play a different role in medical imaging and clinical diagnosis. Medical image fusion can be well integrated information from multiple images, and improve the accuracy of clinical diagnosis and treatment.In 1998. Norden E. Huang from NASA. Chinese-American proposed a new time-frequency analysis theory:Hilbert-Huang transform (HHT). For existing signal analysis as Fourier, or wavelet transform depending on the predetermined basis functions, does not have the data adaptive. HHT transform has fully adaptive, widely used in seismic studies, marine, voice, and mechanical failure analysis field. It used empirical mode decomposition (EMD) to decompose a complex signal into a series of intrinsic mode function (IMF) combined. It is suitable for non-linear and non-stable signals. with good time-frequency characteristic based on local characteristics of the signal.With the EMD theory has mature applications in one-dimensional signal processing, domestic and foreign scholars extend it to two-dimensional image processing, called the Two-dimensional empirical mode decomposition (BEMD). For the sifting of the BEMD process, there are four core steps:First, search the maxima and minima extreme points of the image. Second, choose the envelope surface interpolation function. This needs to consider surface smoothness and computational speed, also need to tolerate the irregular data points, and can be good to avoid the overshoot phenomenon. Third, research the stopping criteria for sifting and decomposition. Fourth, solve the border problem. When we do the decomposition process, if data dispersal at the end of the lower and upper envelopes, it will dispersal from the end to the inner gradually, and 'pollution' the whole data with the sifting running, resulting in decomposition serious distortion.In this papers, consider the medical images' own characteristics, I proposed nearest 8 neighborhood searching method to extract the extreme points, interpolation method based on Delaunay triangulation.'SD'values and fixed sifting number as the stopping criteria, and the even extension to solve the image boundary effect as the 4 key BEMD sifting algorithm to solve the above problems. It is also in line with the needs of medical image fusion.The prime task of this paper is to do image fusion research based on BEMD method. We do the BEMD image fusion experiments with the weighting coeffient fusion rules. On this basis, proposed a new medical image fusion algorithm, it is based on empirical mode decomposition and principal component analysis (EMD-PCA) method. This method using principal component analysis on the intrinsic mode functions after EMD decomposition. to calculate the eigenvalues and eigenvectors, then according to the proportion degree to data fusion. Also medical image fusion evaluation criteria were introduced, and compare the final image with the traditional image fusion method for the quantitative and qualitative comparison. The series of evaluation proved that the algorithm used in this paper enhances spectral information and details, showed that the new fusion method is better.At the last part of the paper, the article summarizes the results of this study, pointed out that EMD is a good time and frequency signal analysis and processing tool. Also analyzes the deficiencies of the EMD theory, and finally predicts the trends and directions of EMD theory.