Rui Zhang , Haoze Sun , Lizhen Deng , Hu Zhu , Wei Qian
{"title":"Medical image fusion via decoupled representation and component-wise regularization learning","authors":"Rui Zhang , Haoze Sun , Lizhen Deng , Hu Zhu , Wei Qian","doi":"10.1016/j.bspc.2024.106859","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424009170","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.