Medical image fusion of multimoding based on wavelet transformation / 中国医学装备

ABSTRACT

Objective: To fuse many non-homologous medical images based on wavelet transformation, and integrate and stand out complementation information, and strengthen image quality, and reduce redundancy so as to enhance the precision of clinically auxiliary diagnosis and treatment for medical image. Methods: Through studied and researched the relevant knowledge of wavelet theory in the application of image fusion to proposed a improved fusion algorithm by mutual combination for maximum low frequency energy and maximum high frequency variance. The fusion experiment of non-homologous standard medical image, including CT, MRI and multi-focus images, were implemented, and their data were compared. And then, the performance of image fusion was compared and analyzed under different fusion rule and different fusion method. Results: In the two kinds of contrastive analysis experiments, the fusion image which depended on algorithm included more abundantly effective information amount of source image, and the luminance of image was reasonable enhanced. Besides, the mean value, mutual information and information entropy of fusion image were optimal. Conclusion: The fusion image which comes from multimoding medical image algorithm has better visual effects and quantization indicator, and it has strengthener fused performance. Therefore, it can reflect the effectiveness of the method.