Multimodal Medical Image Fusion Using Discrete Ripplet Transform and Intersecting Cortical Model

Abstract

Image fusion is the process of combining multiple images of the same scene into a single fused image. It preserves the full content information and retains the important features from each of the original images. In this paper, a fusion algorithm based on Discrete Ripplet Transform (DRT) and Intersecting Cortical Model (ICM) is proposed. The registered CT and MRI images are transformed using DRT – a Multiscale Geometric Analysis (MGA) tool. ICM is used to make an intelligent fusion decision to obtain the fusion coefficients in all the subbands of the ripplet transform. The fused image is obtained by applying inverse DRT to the fused coefficients. The proposed algorithm is compared with the existing fusion methods. The evaluation metrics used for comparison are entropy, standard deviation and average gradient. The simulation results show that the proposed algorithm is very effective in fusing multimodal medical images.