A Semantic-based Medical Image Fusion Approach
This work addresses the challenge for clinicians in analyzing multimodal medical images by improving fusion quality, though it appears incremental as it builds on existing fusion methods with a semantic focus.
The authors tackled the problem of semantic loss in medical image fusion by proposing a new evaluation index and a Fusion W-Net (FW-Net), which greatly reduces semantic information loss and shows better visual effects compared to five state-of-the-art approaches.
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 work, we propose a new evaluation index to measure the semantic loss of fused image, and put forward 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 better visual effects in contrast to five state-of-art approaches. Our approach and tool have great potential to be applied in the clinical setting.