Research On Multi-modality Medical Image Fusion In Technology

The improved living conditions and enhanced consciousness of Self Health Care of people, make them pay more attention to their heath. Nowadays, medical service is developing rapidly and medical imaging equipment are changing for the better day by day.According to the needs of different medical equipment,there are different types of two-dimensional medical images. For example, CT image can make a video picture of the body, which can clearly display the anatomic structure and then PET images can display function in the human body. Combining the anatomy imaging with function imaging will realize complementary advantages and make the best of Image fusion technology.Though traditional Image fusion technology achieved conspicuous results, many problems still exist: There are no uniform rules of image fusion, there are algorithm difference for different targets etc. This thesis is mainly aiming at 3 groups of brain Image fusion, and these medical images groups are CT and MRI, MRI and SPECT, MRI and PET.This thesis firstly explained the background and significance the research subject,as well as the development status of image fusion technology. And then,the thesis introduced the imaging theory and features of medical image in detail, and generally concluded the procedures of image fusion, and drew a general framework for image fusion. Meanwhile,the thesis made three categories of evaluation criterion,for different levels of image fusion.This paper mainly introduces the medical image fusion algorithm based on wavelet transform and Curvelet transform. Fusion image based on wavelet transform without redundant information in the end, but its limitations is the direction, in the process of decompose,nevertheless,image fusion algorithm based on Curvelet transform has more direction. Therefore, it put forward a new algorithm that low sub-band of decomposition using weighted average fusion algorithm and high sub-band using regional energy maximum algorithm of Curvelet transform on the basis of the wavelet transform. Through the brain images in MRI/SPECT, MRI/PET continuously in ten groups are fusing. It is concluded that a group of average according to evaluation criteria of objective.Finally, comparing the algorithm to image fusion algorithm in common in this paper that through subjective and objective evaluation criteria have proved the quality of the fused images is good in this paper’s algorithm, better than other algorithms, and has great significance in clinical medicine.