期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 80, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2022.104382
关键词
Magnetic resonance and nuclear medicine; Multimodal medical images; Multiscale dense network; Multiscale residual attention network
In this study, a multiscale dense residual attention network (MDRANet) is proposed for magnetic resonance and nuclear medicine image fusion. The fusion results obtained through MDRANet have richer details and better objective metrics compared to reference algorithms.
Magnetic resonance and nuclear medicine images are the two categories of multimodal medical images. Magnetic resonance images reveal physiological anatomical information of patients, and nuclear medicine images accurately show tissue lesion information. Through medical image fusion algorithms, these fusion images containing both tissue lesion information and physiological anatomical information are obtained to provide sufficient information for clinical medical technologies. However, most existing fusion algorithms are based on mathematical transform domains, and these fusion results have the weaknesses of blurred edges, color distortion and detail loss. To address these problems, a multiscale dense residual attention network (MDRANet) is proposed and applied to magnetic resonance and nuclear medicine image fusion. MDRANet combines multiscale dense network and multiscale residual attention network to extract and enhance deep features. Moreover, four different loss functions are used to optimize MDRANet and improve the fusion quality. The experimental results show that the fusion results of our proposed algorithm have richer details and better objective metrics compared with the reference algorithms.
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