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

A Novel adaptive optimization of Dual-Tree Complex Wavelet Transform for Medical Image Fusion