Discrete Wavelet Transform and Bird Swarm Optimized Bayesian Multimodal Medical Image Fusion

Abstract

A novel Bayesian image fusion scheme using bird swarm optimization algorithm (BSA) is being proposed here.
The medical Image fusion is progressed using the MRI brain images taken from the BRATS database and the
source images of different modalities are fused effectively to present an information rich fused image. The source
images are subjected to the Haar discrete wavelet transform (DWT) and the Bayesian fusion is performed using
the Bayesian parameter, which is determined optimally using the BSA optimization. The analysis reveals that the
method outperformed the three existing methods of fusion that is nonsubsampled contourlet transform (NSCT),
cascaded static wavelet (SWT) and NSCT ,that is (SWT-NSCT) and Holoentropy and SP-Whale Optimization
method (HW Fusion) with improved values of mutual information(1.4764),peak signal-to-noise ratio (37.2114)
and root mean square error (9.9341) .