最佳质量传输和图像配准

S. Haker, A. Tannenbaum
{"title":"最佳质量传输和图像配准","authors":"S. Haker, A. Tannenbaum","doi":"10.1109/VLSM.2001.938878","DOIUrl":null,"url":null,"abstract":"Image registration is the process of establishing a common geometric reference frame between two or more data sets from the same or different imaging modalities possibly taken at different times. In the context of medical imaging and in particular image-guided therapy, the registration problem consists of finding automated methods that align multiple data sets with each other and with the patient. We propose a method of elastic registration based on the Monge-Kantorovich problem of optimal mass transport.","PeriodicalId":445975,"journal":{"name":"Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Optimal mass transport and image registration\",\"authors\":\"S. Haker, A. Tannenbaum\",\"doi\":\"10.1109/VLSM.2001.938878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Image registration is the process of establishing a common geometric reference frame between two or more data sets from the same or different imaging modalities possibly taken at different times. In the context of medical imaging and in particular image-guided therapy, the registration problem consists of finding automated methods that align multiple data sets with each other and with the patient. We propose a method of elastic registration based on the Monge-Kantorovich problem of optimal mass transport.\",\"PeriodicalId\":445975,\"journal\":{\"name\":\"Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VLSM.2001.938878\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSM.2001.938878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 18

摘要

图像配准是在可能在不同时间拍摄的相同或不同成像方式的两个或多个数据集之间建立共同几何参考框架的过程。在医学成像,特别是图像引导治疗的背景下,注册问题包括找到自动方法,使多个数据集彼此对齐并与患者对齐。提出了一种基于最优质量输运的Monge-Kantorovich问题的弹性配准方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal mass transport and image registration
Image registration is the process of establishing a common geometric reference frame between two or more data sets from the same or different imaging modalities possibly taken at different times. In the context of medical imaging and in particular image-guided therapy, the registration problem consists of finding automated methods that align multiple data sets with each other and with the patient. We propose a method of elastic registration based on the Monge-Kantorovich problem of optimal mass transport.
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