An Improved Medical Image Fusion Method Based on PCNN in NSST Domain

Abstract

We present a novel fusion method based on improved pulse-coupled neural networks (PCNN) model in non-subsampled shearlet transform (NSST) domain for wholebody PET/CT images. Firstly, source images are decomposed using NSST into one low-pass sub-band and several highpass sub-bands. Then, an improved PCNN is used in highpass sub-bands where energy of edge and average gradient are as external input and linking strength respectively. Maximum region energy (MRE) and maximum selection (MS) rules are as fusion rules for high-and low-pass sub-bands respectively. Finally, inverse NSST is adopted to produce fused result. Experiments show the superiority of our method.