Medical image fusion quality assessment based on conditional generative adversarial network - Tang, Lu; Hui, Yu; Yang, Hang; Zhao, Yinghong; Tian, Chuangeng (Frontiers in Neuroscience, 2022)
Multimodal medical image fusion (MMIF) has been proven to effectively improve the efficiency of disease diagnosis and treatment. However, few works have explored dedicated evaluation methods for MMIF. This paper proposes a novel quality assessment method for MMIF based on the conditional generative adversarial networks. First, with the mean opinion scores (MOS) as the guiding condition, the feature information of the two source images is extracted separately through the dual channel Encoder-Decoder. The features of different levels in the Encoder-Decoder are hierarchically input into the Self-Attention Feature Block, which is a fusion strategy for self-identifying favorable features. Then, the discriminator is used to improve the fusion objective of the generator. Finally, we calculate the structural similarity index between the fake image and the true image, and the MOS corresponding to the maximum result will be used as the final assessment result of the fused image quality. Based on the established MMIF database, the proposed method achieves the state-of-the-art performance among the comparison method, with excellent agreement with subjective evaluations, indicating that the method is effective in the quality assessment of medical fusion images.