期刊
SENSORS
卷 20, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/s20082169
关键词
medical image fusion; convolutional neural network; image pyramid; multi-scale decomposition
资金
- National Natural Science Foundation of China [61803061, 61906026]
- National Nuclear Energy Development Project of China [18zg6103]
- Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201800603]
- Chongqing Natural Science Foundation [cstc2018jcyjAX0167]
- Common Key Technology Innovation Special of Key Industries of Chongqing science and Technology Commission [cstc2017zdcy-zdyfX0067, cstc2017zdcy-zdyfX0055, cstc2018jszx-cyzd0634]
- Artificial Intelligence Technology Innovation Significant Theme Special Project of Chongqing science and Technology Commission [cstc2017rgzn-zdyfX0014, cstc2017rgzn-zdyfX0035]
Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high visual quality and clear structure details, this paper proposes a convolutional neural network (CNN) based medical image fusion algorithm. The proposed algorithm uses the trained Siamese convolutional network to fuse the pixel activity information of source images to realize the generation of weight map. Meanwhile, a contrast pyramid is implemented to decompose the source image. According to different spatial frequency bands and a weighted fusion operator, source images are integrated. The results of comparative experiments show that the proposed fusion algorithm can effectively preserve the detailed structure information of source images and achieve good human visual effects.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据