通过解耦表示和分量正则化学习实现医学图像融合

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Rui Zhang , Haoze Sun , Lizhen Deng , Hu Zhu , Wei Qian
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引用次数: 0

摘要

医学图像融合在各种疾病的精确诊断、治疗计划和随访研究中发挥着重要作用。虽然基于卷积稀疏编码的医学图像融合取得了巨大进步,但现有方法仍受限于源医学图像之间难以解决的冗余信息交互问题。在本文中,我们提出了一种基于分解组件方案的简单而有效的表示和正则化学习方法,具有很高的性能竞争力。我们通过解耦表示学习构建了更紧凑的信息交互,同时缓解了融合组件纠缠中的冗余问题。然后,我们自适应地利用两种不同的正则化算子来分别描述两个不同的分量,从而描述了基于解耦原理的结构启发式差异。此外,我们还结合了交替乘法(ADMM)算法和共轭梯度(CG)方法来优化我们提出的模型。实验证明,与最先进的方法相比,我们提出的方法在效率和融合性能方面都有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medical image fusion via decoupled representation and component-wise regularization learning
Medical image fusion plays an important role in the precise diagnosis, treatment planning, and follow-up studies of various diseases. While tremendous improvements in medical image fusion based on convolution sparse coding have been achieved, existing methods are still limited by the intractable redundancy information interaction between source medical images. In this paper, we propose an easy yet effective representation and regularization learning method based on decomposed components scheme with high competitive performance. We construct more compact information interactions by decoupled representation learning, which simultaneously mitigates the problem of redundancy in fusion component entanglement. And then two different regularization operators are adaptively exploited to depict two different components separately, which describe the structural-inspired difference based on the decoupled principle. Furthermore, we combine the alternating direction method of multipliers (ADMM) algorithm and the conjugate gradient (CG) method to optimize our proposed model. Our experiments demonstrate that our proposed method has significant improvements in efficiency and fusion performance against the state-of-the-art methods.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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