  In recent years, many research achievements are made in the medical image
fusion field. Fusion is basically extraction of best of inputs and conveying it
to the output. Medical Image fusion means that several of various modality
image information is comprehended together to form one image to express its
information. The aim of image fusion is to integrate complementary and
redundant information. In this paper, a multimodal image fusion algorithm based
on the dual-tree complex wavelet transform (DT-CWT) and adaptive particle swarm
optimization (APSO) is proposed. Fusion is achieved through the formation of a
fused pyramid using the DTCWT coefficients from the decomposed pyramids of the
source images. The coefficients are fused by the weighted average method based
on pixels, and the weights are estimated by the APSO to gain optimal fused
images. The fused image is obtained through conventional inverse dual-tree
complex wavelet transform reconstruction process. Experiment results show that
the proposed method based on adaptive particle swarm optimization algorithm is
remarkably better than the method based on particle swarm optimization. The
resulting fused images are compared visually and through benchmarks such as
Entropy (E), Peak Signal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE),
Standard deviation (SD) and Structure Similarity Index Metric (SSIM)
computations.
