CT and MRI image fusion via dual-branch GAN Online publication date: Tue, 27-Jun-2023
by Wenzhe Zhai; Wenhao Song; Jinyong Chen; Guisheng Zhang; Qilei Li; Mingliang Gao
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 42, No. 1, 2023
Abstract: CT and MRI image fusion is a popular research field that plays a vital role in clinical diagnosis. To retain more salient features and complementary information from source images, we propose a dual-branch generative adversarial network (DBGAN) to fuse the CT and MRI images. The proposed DBGAN is designed in a dual branching structure schema, which consists of a couple of generators and discriminators. The generators and discriminators establish a generative adversarial relationship so that the fused images generated by the generators are indistinguishable from the discriminators. Furthermore, we employ the multiscale extraction module (MEM) and self-attention module (SAM) in the generators to enhance the salient features and detailed information of the fused images. The subjective and objective evaluation demonstrate the superiority of the proposed method over the state-of-the-art methods.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook