Multi-modal medical image fusion via multi-dictionary and truncated Huber filtering
Multi-modal medical image fusion provides comprehensive and objective descriptions of
lesions for clinical medical assistance. However, retaining useful information while …
lesions for clinical medical assistance. However, retaining useful information while …
Two-scale multimodal medical image fusion based on guided filtering and sparse representation
C Pei, K Fan, W Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Medical image fusion techniques primarily integrate the complementary features of different
medical images to acquire a single composite image with superior quality, reducing the …
medical images to acquire a single composite image with superior quality, reducing the …
Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering
Q Hu, S Hu, F Zhang - Signal Processing: Image Communication, 2020 - Elsevier
Sparse representation (SR) has been widely used in image fusion in recent years. However,
source image, segmented into vectors, reduces correlation and structural information of …
source image, segmented into vectors, reduces correlation and structural information of …
Coupled feature learning for multimodal medical image fusion
Multimodal image fusion aims to combine relevant information from images acquired with
different sensors. In medical imaging, fused images play an essential role in both standard …
different sensors. In medical imaging, fused images play an essential role in both standard …
EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer
W Wang, J He, H Liu - Computers in Biology and Medicine, 2024 - Elsevier
Multimodal medical image fusion fuses images with different modalities and provides more
comprehensive and integrated diagnostic information. However, current multimodal image …
comprehensive and integrated diagnostic information. However, current multimodal image …
DFENet: A dual-branch feature enhanced network integrating transformers and convolutional feature learning for multimodal medical image fusion
W Li, Y Zhang, G Wang, Y Huang, R Li - Biomedical Signal Processing and …, 2023 - Elsevier
In recent times, several medical image fusion techniques based on the convolutional neural
network (CNN) have been proposed for various medical imaging fusion tasks. However …
network (CNN) have been proposed for various medical imaging fusion tasks. However …
SS-SSAN: a self-supervised subspace attentional network for multi-modal medical image fusion
Y Zhang, R Nie, J Cao, C Ma, C Wang - Artificial Intelligence Review, 2023 - Springer
Multi-modal medical image fusion (MMIF) is used to merge multiple modes of medical
images for better imaging quality and more comprehensive information, such that enhancing …
images for better imaging quality and more comprehensive information, such that enhancing …
MRSCFusion: Joint residual Swin transformer and multiscale CNN for unsupervised multimodal medical image fusion
X Xie, X Zhang, S Ye, D Xiong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
It is crucial to integrate the complementary information of multimodal medical images for
enhancing the image quality in clinical diagnosis. Convolutional neural network (CNN) …
enhancing the image quality in clinical diagnosis. Convolutional neural network (CNN) …
SIMFusion: A semantic information-guided modality-specific fusion network for MR Images
X Zhang, A Liu, G Yang, Y Liu, X Chen - Information Fusion, 2024 - Elsevier
Multi-modal medical image fusion aims to integrate distinct imaging modalities to yield more
comprehensive and precise medical images, which can benefit the subsequent image …
comprehensive and precise medical images, which can benefit the subsequent image …
Multimodal medical image fusion using adaptive co-occurrence filter-based decomposition optimization model
Motivation Medical image fusion has developed into an important technology, which can
effectively merge the significant information of multiple source images into one image. Fused …
effectively merge the significant information of multiple source images into one image. Fused …