Review of CT and PET image fusion using hybrid algorithm

Gauri D. Patne, P. A. Ghonge, K. Tuckley
{"title":"Review of CT and PET image fusion using hybrid algorithm","authors":"Gauri D. Patne, P. A. Ghonge, K. Tuckley","doi":"10.1109/I2C2.2017.8321861","DOIUrl":null,"url":null,"abstract":"In image processing Image Fusion used in medical images for accuracy of successful diagnosis of disease. Image fusion process gives highly informative image as it combines the information from two or more images into a single image. This paper explains the concept of image fusion using hybrid algorithm for multimodality medical images The structural details of body parts, like CT, MRI, and functional details of cell activity in the organ, like PET are important for analysis. So, this work shows fusion of CT and PET images. Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Discrete Curvelet Transformation (DCT) and Principal Component Analysis (PCA) are most widely used image fusion algorithms. Hybrid algorithm is developed by integrating the conventional and advance fusion methods to overcome their demerits and enhance the image processing qualities The various algorithms are studied, observed and compared the results using the performance MSE, PSNR and ENTROPY.","PeriodicalId":288351,"journal":{"name":"2017 International Conference on Intelligent Computing and Control (I2C2)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Intelligent Computing and Control (I2C2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2C2.2017.8321861","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

In image processing Image Fusion used in medical images for accuracy of successful diagnosis of disease. Image fusion process gives highly informative image as it combines the information from two or more images into a single image. This paper explains the concept of image fusion using hybrid algorithm for multimodality medical images The structural details of body parts, like CT, MRI, and functional details of cell activity in the organ, like PET are important for analysis. So, this work shows fusion of CT and PET images. Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Discrete Curvelet Transformation (DCT) and Principal Component Analysis (PCA) are most widely used image fusion algorithms. Hybrid algorithm is developed by integrating the conventional and advance fusion methods to overcome their demerits and enhance the image processing qualities The various algorithms are studied, observed and compared the results using the performance MSE, PSNR and ENTROPY.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CT与PET图像融合的混合算法综述
在图像处理中,图像融合用于医学图像的准确诊断。图像融合过程将两幅或多幅图像的信息合并成一幅图像,从而获得信息量很大的图像。本文阐述了使用混合算法对多模态医学图像进行图像融合的概念。身体部位的结构细节,如CT、MRI,以及器官中细胞活动的功能细节,如PET,对分析很重要。因此,这个作品展示了CT和PET图像的融合。离散小波变换(DWT)、平稳小波变换(SWT)、离散曲线变换(DCT)和主成分分析(PCA)是目前应用最广泛的图像融合算法。将传统的融合方法和先进的融合方法相结合,开发了混合算法,克服了各自的缺点,提高了图像处理质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Automated coconut tree climber Homomorphic encryption-state of the art Automatic toll payment, alcohol detection, load and vehicle information using Internet of things & mailing system Performance prediction using modified clustering techniques with fuzzy association rule mining approach for retail Enhancing pattern recognition in social networking dataset by using bisecting KMean
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1