Medical image fusion based on spiking cortical model

Abstract

Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, non-invasive diagnosis, and treatment planning. Although numerous researches have been done in developing various medical image fusion algorithms, the disadvantage of these approaches is that they lack universality in dealing with different kinds of medical images. To address this problem, we have proposed a novel method of medical image fusion using the spiking cortical model (SCM) for the first time. In the paper, the mathematical model of SCM is firstly described, and then image fusion algorithm with SCM is introduced in detail. To show that the SCM based fusion method can deal with multimodal medical images, we have used three pairs of medical images with different modalities in the simulation experiments and made comparisons among the proposed method and the state-of-art fusion methods such as Laplacian pyramid, Contrast pyramid, Morphological pyramid and Ratio pyramid. The performance of various methods is investigated using such image assessment metrics as Mutual Information (MI), the edge preservation values (QAB/F), the Local Structural Similarity (LSSIM) and the Universal Image Quality Index (UIQI). The experimental results show that our proposed method outperforms other methods in both visual effect and objective evaluation. It demonstrates that the SCM based method is a highly effective method for multi-modal medical image fusion due to its versatility and stability.