A medical image fusion method based on convolutional neural networks

By Yu Liu, Xun Chen, Juan Cheng, Hu Peng. Fusion, 2017

370 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/d2a2fc7bb06de0388d2ed05ed1fe3c38178aa367

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jiayi Ma
2. Linfeng Tang
3. Hao Zhang
4. Han Xu
5. Hui Li
6. Xiaojie Guo
7. Yong Ma
8. Junjun Jiang

In Media Technology

1. Jiayi Ma
2. Linfeng Tang
3. Han Xu
4. Hao Zhang
5. Hui Li
6. Xiaojie Guo
7. Yong Ma
8. Junjun Jiang

Citations in the corpus

• SDNet: A Versatile Squeeze-and-Decomposition Network for Real-Time Image Fusion (2021, methodology): “In medical image fusion, Liu et al. (2017) used a neural network to generate the weight map that integrates pixel activity levels of two source images, while the fusion process is conducted in a multi-scale manner via image pyramids.”
• U2Fusion: A Unified Unsupervised Image Fusion Network (2020, methodology): “We compare U2Fusion with RPCNN [44], CNN [16], PA-PCNN [45], and NSCT [6] on the Harvard dataset.”
• Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain (2019, methodology): “onal matching pursuit (SR-SOMP) method [4], the guided filtering (GF) method [11], the phase congruency and directive contrast in NSCT domain (NSCT-PCDC) method [22], the NSCT-RPCNN method [23], the LP-SR method [8], the CTD-SR method [28], the LLF-IOI method [29], and the LP-CNN method [30].”
• GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, methodology): “In medical image fusion, Liu et al. [35] introduced the neural network to implement measurement of activity information and then used the image pyramids to complete the fusion process.”
• FusionDN: A Unified Densely Connected Network for Image Fusion (2020, methodology): “In (Liu et al. 2017b), for fusing multi-focus images, a deep CNN trained by high-quality image patches and corresponding artificially made blurred versions is adopted to encode a direct mapping between source images and the focus map.”
• SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer (2022, methodology): “One form of CNN participating in image fusion employs the pre-trained networks to realize activity level measurement and generate a weight map for hand-crafted features [5], [43].”
• DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion (2020, methodology): “In [26], Liu et al . proposed a medical image fusion method based on convolutional neural networks (CNNs).”