An Improved Hybrid Network With a Transformer Module for Medical Image Fusion

• Published in: IEEE journal of biomedical and health informatics 2023-07, Vol.27 (7), p.1-12
• Main Authors: Liu, Yanyu, Zang, Yongsheng, Zhou, Dongming, Cao, Jinde, Nie, Rencan, Hou, Ruichao, Ding, Zhaisheng, Mei, Jiatian
• Format: Article
• Language: English
• Subjects: Coders; Computer vision; Datasets; Diagnosis, Computer-Assisted; Discrete wavelet transforms; Electric Power Supplies; Encoders-Decoders; Feature extraction; Humans; Image fusion; Image Processing, Computer-Assisted; Image quality; Image reconstruction; Information retrieval; Information Storage and Retrieval; Medical diagnosis; Medical diagnostic imaging; Medical imaging; Modules; self-adaptive weight fusion; self-reconstruction; Signal quality; transformer; Transformers; Transforms
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3264819

Medical image fusion technology is an essential component of computer-aided diagnosis, which aims to extract useful cross-modality cues from raw signals to generate high-quality fused images. Many advanced methods focus on designing fusion rules, but there is still room for improvement in cross-modal information extraction. To this end, we propose a novel encoder-decoder architecture with three technical novelties. First, we divide the medical images into two attributes, namely pixel intensity distribution attributes and texture attributes, and thus design two self-reconstruction tasks to mine as many specific features as possible. Second, we propose a hybrid network combining a CNN and a transformer module to model both long-range and short-range dependencies. Moreover, we construct a self-adaptive weight fusion rule that automatically measures salient features. Extensive experiments on a public medical image dataset and other multimodal datasets show that the proposed method achieves satisfactory performance.