Medical image fusion method based on dense block and deep convolutional generative adversarial network

Abstract

Medical image fusion techniques can further improve the accuracy and time efficiency of clinical diagnosis by obtaining comprehensive salient features and detail information from medical images of different modalities. We propose a novel medical image fusion algorithm based on deep convolutional generative adversarial network and dense block models, which is used to generate fusion images with rich information. Specifically, this network architecture integrates two modules: an image generator module based on dense block and encoder–decoder and a discriminator module. In this paper, we use the encoder network to extract the image features, process the features using fusion rule based on the Lmax norm, and use it as the input of the decoder network to obtain the final fusion image. This method can overcome the weaknesses of the active layer measurement by manual design in the traditional methods and can process the information of the intermediate layer according to the dense blocks to avoid the loss of information. Besides, this paper uses detail loss and structural similarity loss to construct the loss function, which is used to improve the extraction ability of target information and edge detail information related to images. Experiments on the public clinical diagnostic medical image dataset show that the proposed algorithm not only has excellent detail preserve characteristics but also can suppress the artificial effects. The experiment results are better than other comparison methods in different types of evaluation.