Multimodal Medical Image Fusion Using Redundant Discrete Wavelet Transform

Medical image fusion has revolutionized medical analysis by improving the precision and performance of computer assisted diagnosis. In this research, a fusion algorithm is proposed to combine pairs of multispectral magnetic resonance imaging such as T1, T2 and proton density brain images. The proposed algorithm utilizes different features of redundant discrete wavelet transform, mutual information based non-linear registration and entropy information to improve performance. Experiments on the BrainWeb database show that the proposed fusion algorithm preserves both edge and component information, and provides improved performance compared to existing discrete wavelet transform based fusion algorithms.