Medical image fusion using a modified shark smell optimization algorithm and hybrid wavelet-homomorphic filter

Abstract

Medical image fusion is a principal category in the medical applications which has great impacts on the final diagnosis results. In this study, a hybrid optimization technique is presented for developing a high efficiency technique for the fusion of the medical images. The presented method uses both advantages of the wavelet transform and the homomorphic filter for improving the system efficiency. For achieving the optimal values of the system, a new optimization algorithm based on two new introduced methods, shark smell optimization algorithm and world cup optimization algorithm is introduced. The new algorithm is then applied to the wavelet part of the system to get the optimal values. Simulations are applied on two classes of five clinical images including MR-CT, MR-SPECT, and MR-PET the results are compared with six popular methods. The final results showed that the proposed system has higher efficiency from the studied methods.