Optimum weighted multimodal medical image fusion using particle swarm optimization

Medical image fusion is a technique that maps information from multiple image modalities into a single image. In past, several multimodal fusion approaches were developed by various researchers, even so, adaptive fusion and robustness are challenging tasks in clinical diagnosis. In this paper, we developed an optimum weighted average fusion (OWAF) for multimodal medical image fusion to improve the multimodal mapping performance. In our approach, conventional discrete wavelet transform (DWT) is used for the decomposition of input multiple modalities into various subgroups. The resultant energy bands were weighted using optimum weights, attained using well known particle swarm optimization algorithm (PSO). Our proposed approach was tested over MRI-SPECT, MRI-PET and MRI-CT image fusion and we used 20 sets of MR/SPECT, 20 sets of MR/PET and 18 sets of MR/CT for our method validation. The quantitative evaluation were performed using established fusion performance metrics such as structural similarity index measure (SSIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), entropy, mutual information (MI), and Edge-based similarity metric(Q AB/F ). Robustness of our proposed fusion approach is tested over gaussian and speckle noise in all the input modalities. The computational performance of our OAWF with PSO algorithm is tested in terms of computational time. The OWAF showed superior performance than existing conventional fusion approaches in terms of information mapping, edge quality and structural similarity in MR/PET, MR/SPECT and MR/CT images in both normal and noisy fusion backgrounds.