Background and objectives Over the past two decades, medical imaging has been
extensively apply to diagnose diseases. Medical experts continue to have difficulties for …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

The medical image fusion is the process of coalescing multiple images from multiple
imaging modalities to obtain a fused image with a large amount of information for increasing …

Although many powerful convolutional neural networks (CNN) have been applied to various
image processing fields, due to the lack of datasets for network training and the significant …

The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …

Multimodal medical image fusion is effectuated to minimize the redundancy while
augmenting the necessary information from the input images acquired using different …

In this paper, a novel fusion algorithm based on the adaptive dual-channel unit-linking pulse
coupled neural network (PCNN) for infrared and visible images fusion in nonsubsampled …

At present, multimodal medical image fusion technology has become an essential means for
researchers and doctors to predict diseases and study pathology. Nevertheless, how to …

Medical image fusion plays an important role in clinical applications such as image-guided
surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. The …