基于同步各向异性扩散方程和DT-CWT分解的MRI和CT图像融合

Vijayalakshmi Aakaaram, Srinvas Bachu
{"title":"基于同步各向异性扩散方程和DT-CWT分解的MRI和CT图像融合","authors":"Vijayalakshmi Aakaaram, Srinvas Bachu","doi":"10.1109/STCR55312.2022.10009173","DOIUrl":null,"url":null,"abstract":"Medical image fusion plays the major role in many applications including brain tumor segmentation, and classification. But the conventional methods are suffering with colour artifacts. Thus, this article proposes a novel magnetic resonance imaging (MRI) and computerized tomography (CT) based multi modal medical image fusion using synchronized anisotropic diffusion equation (SADE) with dual tree dual-tree complex wavelet transform (DT-CWT) decomposition. Initially, source images are divided into multiple bands by using DT-CWT approach. In addition, SADE process is applied to extract the approximate and detailed layers. Further, principal component analysis (PCA) is applied to extract the eigen vectors. Finally, PCA fusion rule is applied to get the fused outcome. The simulation results show that proposed fusion results shows better subjective and object performance as compared to conventional fusion methods.","PeriodicalId":338691,"journal":{"name":"2022 Smart Technologies, Communication and Robotics (STCR)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MRI and CT Image Fusion using Synchronized Anisotropic Diffusion Equation with DT-CWT Decomposition\",\"authors\":\"Vijayalakshmi Aakaaram, Srinvas Bachu\",\"doi\":\"10.1109/STCR55312.2022.10009173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical image fusion plays the major role in many applications including brain tumor segmentation, and classification. But the conventional methods are suffering with colour artifacts. Thus, this article proposes a novel magnetic resonance imaging (MRI) and computerized tomography (CT) based multi modal medical image fusion using synchronized anisotropic diffusion equation (SADE) with dual tree dual-tree complex wavelet transform (DT-CWT) decomposition. Initially, source images are divided into multiple bands by using DT-CWT approach. In addition, SADE process is applied to extract the approximate and detailed layers. Further, principal component analysis (PCA) is applied to extract the eigen vectors. Finally, PCA fusion rule is applied to get the fused outcome. The simulation results show that proposed fusion results shows better subjective and object performance as compared to conventional fusion methods.\",\"PeriodicalId\":338691,\"journal\":{\"name\":\"2022 Smart Technologies, Communication and Robotics (STCR)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Smart Technologies, Communication and Robotics (STCR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/STCR55312.2022.10009173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Smart Technologies, Communication and Robotics (STCR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/STCR55312.2022.10009173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

医学图像融合在脑肿瘤分割、分类等诸多应用中发挥着重要作用。但是传统的方法受到彩色伪影的影响。为此,本文提出了一种基于同步各向异性扩散方程(SADE)和双树双树复小波变换(DT-CWT)分解的基于磁共振成像(MRI)和计算机断层扫描(CT)的多模态医学图像融合方法。首先,利用DT-CWT方法将源图像划分为多个波段。此外,应用SADE过程提取近似层和详细层。在此基础上,应用主成分分析(PCA)提取特征向量。最后,应用PCA融合规则得到融合结果。仿真结果表明,与传统的融合方法相比,所提出的融合方法具有更好的主客体性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI and CT Image Fusion using Synchronized Anisotropic Diffusion Equation with DT-CWT Decomposition
Medical image fusion plays the major role in many applications including brain tumor segmentation, and classification. But the conventional methods are suffering with colour artifacts. Thus, this article proposes a novel magnetic resonance imaging (MRI) and computerized tomography (CT) based multi modal medical image fusion using synchronized anisotropic diffusion equation (SADE) with dual tree dual-tree complex wavelet transform (DT-CWT) decomposition. Initially, source images are divided into multiple bands by using DT-CWT approach. In addition, SADE process is applied to extract the approximate and detailed layers. Further, principal component analysis (PCA) is applied to extract the eigen vectors. Finally, PCA fusion rule is applied to get the fused outcome. The simulation results show that proposed fusion results shows better subjective and object performance as compared to conventional fusion methods.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信