In image-based medical decision-making, different modalities of medical images of a given
organ of a patient are captured. Each of these images will represent a modality that will …

Recently, deep learning has been shown effectiveness in multimodal image fusion. In this
paper, we propose a fusion method for CT and MR medical images based on convolutional …

In this letter, a sparse representation (SR) model named convolutional sparsity based
morphological component analysis (CS-MCA) is introduced for pixel-level medical image …

In this paper, we proposed a new end-to-end model, termed as dual-discriminator
conditional generative adversarial network (DDcGAN), for fusing infrared and visible images …

Technology-assisted clinical diagnosis has gained tremendous importance in modern day
healthcare systems. To this end, multimodal medical image fusion has gained great …

Medical image fusion foremost focuses on discovering superior technique on merging
multimodal medical images which plays an important part on clinical analysis and treatment …

Existing image fusion approaches are committed to using a single deep network to solve
different image fusion problems, achieving promising performance in recent years. However …

Medical image fusion of images obtained via different modes can expand the inherent
information of original images, whereby the fused image has a superior ability to display …

Multi-modality medical image fusion technology can integrate the complementary
information of different modality medical images, obtain more precise, reliable and better …

Multi-modality image fusion is a technique that combines information from different sensors
or modalities enabling the fused image to retain complementary features from each modality …