A three-layer decomposition method based on structural texture perception for fusion of CT and MRI images

图像融合 增采样 人工智能 计算机科学 融合 计算机视觉 冗余(工程) 模式识别(心理学) 图像纹理 纹理(宇宙学) 图像处理 图像(数学) 语言学 操作系统 哲学
作者
Rui Zhu,Yong Lü,Xiaoli Zhang,Xiongfei Li,Yuncong Feng
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:88: 105518-105518
标识
DOI:10.1016/j.bspc.2023.105518
摘要

The fusion of computed tomography (CT) images and magnetic resonance imaging (MRI) images has made essential contribution to clinical diagnosis because of its ability of complementary visual information enhancement and redundancy elimination. However, some methods that involve upsampling and downsampling operations may lose information during image processing, which affects the fusion results. In this paper, a medical image fusion algorithm based on structural texture perception is proposed. The proposed method demonstrates enhanced performance in preserving both detailed texture information and energy information of source images. First, the source image is decomposed into a signal strength layer, a base layer, and a texture layer based on the proposed three-layer decomposition framework. Then, the signal strength layers are fused using a saliency detection method based on iterative least squares. The texture layer is fused using the principle of spatial frequency maximization. For the fusion of the base layer, a non-linear function is designed to calculate the fusion weights. Finally, the final fusion result is obtained through image reconstruction. The proposed algorithm is compared with nine state-of-the-art fusion algorithms to verify its superiority. Experimental results show that the proposed method can effectively preserve the intensity information of CT images and the detailed texture of MRI images.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
MchemG应助qq采纳,获得10
1秒前
小二郎应助科研通管家采纳,获得10
1秒前
bkagyin应助科研通管家采纳,获得10
1秒前
1秒前
1秒前
Hello应助科研通管家采纳,获得10
1秒前
在水一方应助科研通管家采纳,获得10
1秒前
1秒前
SHAO应助科研通管家采纳,获得10
1秒前
pcr163应助科研通管家采纳,获得30
1秒前
1秒前
和谐一万完成签到,获得积分10
1秒前
SYLH应助科研通管家采纳,获得30
1秒前
quhayley应助科研通管家采纳,获得10
1秒前
Zhou发布了新的文献求助10
1秒前
科目三应助科研通管家采纳,获得10
1秒前
pcr163应助科研通管家采纳,获得30
2秒前
英姑应助科研通管家采纳,获得10
2秒前
Anita发布了新的文献求助10
2秒前
SYLH应助科研通管家采纳,获得10
2秒前
2秒前
我是老大应助科研通管家采纳,获得10
2秒前
SYLH应助科研通管家采纳,获得10
2秒前
Ava应助科研通管家采纳,获得10
2秒前
充电宝应助科研通管家采纳,获得10
2秒前
神的女人发布了新的文献求助10
2秒前
pcr163应助科研通管家采纳,获得30
2秒前
Liufgui应助科研通管家采纳,获得20
2秒前
褪黑素应助科研通管家采纳,获得10
2秒前
2秒前
2秒前
2秒前
2秒前
2秒前
2秒前
2秒前
SYLH应助科研通管家采纳,获得20
2秒前
MchemG应助Qianyun采纳,获得30
4秒前
4秒前
4秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021