Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion: Introduction

BACKGROUND: There are various applications for medical image fusion schemes in different medical clinics. Here, the generalized version of the homomorphic filtering technique involving the Fourier domain for image and signal processing is a proper method. METHODS: The methods on the wavelet transform proposes some advantages in the discretization of multimodality medical images fusion, conducted in the Fourier spectrum. In the present study, an optimal version of the homomorphic fusion, namely optimum homomorphic wavelet fusion (OHWF) on the hybrid particle swarm and ant colony optimization methods, is presented. The presented OHWF, including some domains including wavelet and logarithmic, and besides, the wavelet allows the OHWF technique to decompose the images in the multi-level process. RESULTS: In this work, the modality one approximation coefficients and the coefficients belong to modality two are presented in adder1. While in the case of adder two, the modality one optimal scaled detailed constant values of modality one and the approximation coefficients refer to modality two are added in conjunction. The pixel-based averaged principle is applied to fuse the address one and two results simultaneously. First, the intended fusion technique is authenticated, applying different fusion assessment metrics for MR-PET, MR-SPECT, MR T1-T2, and MR-CT image fusions. And then, the proposed hybrid particle swarm optimizer (PSO) and ACO algorithms applied to obtain the best image fusion. CONCLUSIONS: The empirical data illustrates that the presented method performs a desiring ability in image fusion in the case of functional and structural data.