Nonrigid registration of breast MR images using residual complexity similarity measure

Azam Hamidi Nekoo, A. Ghaffari, E. Fatemizadeh
{"title":"Nonrigid registration of breast MR images using residual complexity similarity measure","authors":"Azam Hamidi Nekoo, A. Ghaffari, E. Fatemizadeh","doi":"10.1109/IRANIANMVIP.2013.6779987","DOIUrl":null,"url":null,"abstract":"Elimination of motion artifact in breast MR images is a significant issue in pre-processing step before utilizing images for diagnostic applications. Breast MR Images are affected by slow varying intensity distortions as a result of contrast agent enhancement. Thus a nonrigid registration algorithm considering this effect is needed. Traditional similarity measures such as sum of squared differences and cross correlation, ignore the mentioned distortion. Therefore, efficient registration is not obtained. Residual complexity is a similarity measure that considers spatially varying intensity distortions by maximizing sparseness of the residual image. In this research, the results obtained by applying nonrigid registration based on residual complexity, sum of squared differences and cross correlation similarity measures are demonstrated which show more robustness and accuracy of RC comparing with other similarity measures for breast MR images.","PeriodicalId":297204,"journal":{"name":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRANIANMVIP.2013.6779987","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Elimination of motion artifact in breast MR images is a significant issue in pre-processing step before utilizing images for diagnostic applications. Breast MR Images are affected by slow varying intensity distortions as a result of contrast agent enhancement. Thus a nonrigid registration algorithm considering this effect is needed. Traditional similarity measures such as sum of squared differences and cross correlation, ignore the mentioned distortion. Therefore, efficient registration is not obtained. Residual complexity is a similarity measure that considers spatially varying intensity distortions by maximizing sparseness of the residual image. In this research, the results obtained by applying nonrigid registration based on residual complexity, sum of squared differences and cross correlation similarity measures are demonstrated which show more robustness and accuracy of RC comparing with other similarity measures for breast MR images.
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
基于残差复杂度相似度的乳腺MR图像非刚性配准
消除运动伪影在乳房磁共振图像是一个重要的预处理步骤,然后利用图像诊断应用。由于造影剂增强,乳房MR图像受到缓慢变化的强度扭曲的影响。因此,需要一种考虑这种影响的非刚性配准算法。传统的相似性度量,如差的平方和和互相关,忽略了上述的失真。因此,无法获得有效的注册。残差复杂度是一种相似性度量,通过最大化残差图像的稀疏性来考虑空间变化的强度畸变。在本研究中,应用基于残差复杂度、差平方和和相互关联相似度量的非刚性配准得到的结果表明,RC与其他乳房MR图像相似度量相比,具有更高的鲁棒性和准确性。
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 lung CT image segmentation using kernel mean shift analysis A simple and efficient approach for 3D model decomposition MRI image reconstruction via new K-space sampling scheme based on separable transform Fusion of SPECT and MRI images using back and fore ground information Real time occlusion handling using Kalman Filter and mean-shift
×
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