Brain medical image fusion scheme based on shuffled frog‐leaping algorithm and adaptive pulse‐coupled neural network

Abstract

Aiming at the problems of low contrast and blurred edge textures in medical image fusion, a new fusion scheme in non-subsampled contourlet transform (NSCT) domain is proposed to improve the quality of fused brain images which is based on pulse-coupled neural network (PCNN) and shuffled frog-leaping algorithm (SFLA). First, the source images are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSCT; if one of the source images is multicolour, then hue, saturation and brightness (HSI) transform is needed first. Second, different PCNN fusion rules are designed for LF and HF subbands according to their features, respectively. Parameters including decay time constants and amplification factors are optimised by SFLA. Finally, the fused image is reconstructed by inverse NSCT; and if necessary, an inverse HSI transform is needed. Visual and quantitative analysis of experimental results show that the fused image preserves more information of the source images, and the ability of edge retention is strong. The scheme has prominent advantages in mutual information and QAB/F for multimodal brain images, including MRI-PET, MRI-SPECT, and CT-MRI, which proves that it can obtain better visual effect and have strong robustness as well as wide applications.