Multi-Source Information Exchange Encoding With PCNN for Medical Image Fusion

Abstract

Multimodal medical image fusion (MMIF) is to merge multiple images for better imaging quality with preserving different specific features, which could be more informative for efficient clinical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on multi-source information exchange encoding (MIEE) by using Pulse Coupled Neural Network (PCNN). We construct an MIEE model by using two types of PCNN, such that the information of an image can be exchanged and encoded to another image. Then the fusion contributions for each pixel are estimated qualitatively according to a logical comparison of exchanged information. Further, the exchanged information is nonlinearly transformed using an exponential function with a functional parameter. Finally, quantitative fusion contributions are produced through a reverse-proportional operator to the exchanged information. Also, particle swarm optimization-based derivative-free optimization and a total vibration-based derivative optimization are used to optimize the PCNN and functional transform parameters, respectively. Experiments demonstrate that our method gives the best results than other state-of-the-art fusion approaches.