Name
Abstract | ||
|---|---|---|
It is necessary for clinicians to comprehensively analyze patient information from different sources. Medical image fusion is a promising approach to providing overall information from medical images of different modalities. However, existing medical image fusion approaches ignore the semantics of images, making the fused image difficult to understand. In this paper, we put forward a semantic-based medical image fusion methodology, and as an implementation, we propose a Fusion W-Net (FW-Net) for multimodal medical image fusion. The experimental results are promising: the fused image generated by our approach greatly reduces the semantic information loss, and has comparable visual effects in contrast to the state-of-art approaches. Our approach and tool have great potential to be applied in the clinical setting. The source code of FW-Net is available at https://github.com/fanfanda/Medical-Image-Fusion. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Image and Video Processing | Journal |
Volume | Citations | PageRank |
abs/1906.00225 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fanda Fan | 1 | 1 | 1.71 |
| Yunyou Huang | 2 | 2 | 3.46 |
| Lei Wang | 3 | 577 | 46.85 |
| Xingwang Xiong | 4 | 0 | 1.69 |
| Zihan Jiang | 5 | 1 | 2.39 |
| Zhifei Zhang | 6 | 10 | 10.25 |
| Jianfeng Zhan | 7 | 767 | 62.86 |