Optimum Wavelet-Based Homomorphic Medical Image Fusion Using Hybrid Genetic-Grey Wolf Optimization Algorithm

Medical image fusion techniques have been widely
used in various clinical applications. Generalized homomorphic
filters have Fourier domain features of input image. In multi
modal medical image fusion discrete wavelet transform based
techniques provides more features and is performed over Fourier spectrum. In this paper, we proposed a Homomorphic wavelet fusion which is called Optimum Homomorphic Wavelet Fusion (OHWF) using Hybrid Genetic – Grey Wolf Optimization (HGGWO) Algorithm. In OHWF, which consist of logarithmic and
wavelet domain information of input images. The wavelet based
homomorphic fusion consists of multi level decomposition
features of input image. In our proposal, the approximation
coefficients of modality1 (anatomical structure) and optimum
scaled detailed coefficients of modality2 are given to adder1. In
adder 2, the optimum scaled detailed coefficients of modality 1
and approximation coefficients of modality 2 are added together. The resultants of adder 1 and adder 2 are fused together using pixel based averaging rule. Firstly, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with genetic operator. Experimental results show that, the proposed approach outperforms state-of-the-art fusion algorithms in terms of both structural and the functional information in the fused image.