Haar wavelet transform-based optimal Bayesian method for medical image fusion

Image fusion (IF) attracts the researchers in the areas of the medical industry as valuable information could be afforded through the fusion of images that enable the clinical decisions to remain effective. With the aim to render an effective image fusion, this paper concentrates on the Bayesian fusion approach, which is tuned optimally using the proposed Fractional Bird Swarm Optimization (Fractional-BSA). The medical image fusion is progressed using the MRI brain image taken from the BRATS database, and the source images of multimodalities are fused effectively to present an information-rich fused image. The source images are subjected to the Haar wavelet transform, and the Bayesian fusion is performed using the Bayesian parameter, which is determined optimally using the proposed Fractional-BSA optimization. The proposed optimization is the integration of the fractional theory in the standard Bird Swarm Optimization (BSA), which improves the effectiveness of image fusion. Unlike any other existing methods, the proposed Fractional-BSA-based Bayesian Fusion approach renders a good quality and complex-free fusion experience. The analysis reveals that the method outperformed the existing methods with maximal mutual information, maximal peak signal-to-noise ratio (PSNR), minimal root mean square error (RMSE) of 1.5665, 44.0857 dB, and 5.4840, respectively.