Multi-resolution parallel integral projection for fast localization of a straight electrode in 3D ultrasound images

M. Uhercik, J. Kybic, H. Liebgott, C. Cachard
{"title":"Multi-resolution parallel integral projection for fast localization of a straight electrode in 3D ultrasound images","authors":"M. Uhercik, J. Kybic, H. Liebgott, C. Cachard","doi":"10.1109/ISBI.2008.4540925","DOIUrl":null,"url":null,"abstract":"We address the problem of fast and accurate localization of miniature surgical instruments like needles or electrodes using 3D ultrasound (US). An algorithm based on maximizing a parallel integral transform (PIP) can automatically localize line-shaped objects in 3D US images with accuracy on the order of hundreds of micrometers. Here we propose to use a multi-resolution to accelerate the algorithm significantly. We use a maximum function for downsampling to preserve the high intensity voxels of a thin electrode. We integrate the multi-resolution pyramid into a hierarchical mesh-grid search of PIP. The experiments with a tissue mimicking phantom and breast biopsy data show that proposed method works well on real US images. The speed-up is threefold compared to original PIP method with the same accuracy 0.4 mm. A further speed-up up to 16 times is reached by an early stopping of the optimization, at the expense of some loss of accuracy.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2008.4540925","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

Abstract

We address the problem of fast and accurate localization of miniature surgical instruments like needles or electrodes using 3D ultrasound (US). An algorithm based on maximizing a parallel integral transform (PIP) can automatically localize line-shaped objects in 3D US images with accuracy on the order of hundreds of micrometers. Here we propose to use a multi-resolution to accelerate the algorithm significantly. We use a maximum function for downsampling to preserve the high intensity voxels of a thin electrode. We integrate the multi-resolution pyramid into a hierarchical mesh-grid search of PIP. The experiments with a tissue mimicking phantom and breast biopsy data show that proposed method works well on real US images. The speed-up is threefold compared to original PIP method with the same accuracy 0.4 mm. A further speed-up up to 16 times is reached by an early stopping of the optimization, at the expense of some loss of accuracy.
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
三维超声图像中直线电极的多分辨率平行积分投影快速定位
我们解决了使用3D超声(US)快速准确定位针头或电极等微型手术器械的问题。一种基于最大化平行积分变换(PIP)的算法可以自动定位三维美国图像中的线形物体,精度在数百微米量级。在这里,我们提出使用多分辨率来显著加速算法。我们使用最大函数进行下采样,以保持薄电极的高强度体素。我们将多分辨率金字塔整合到PIP的分层网格搜索中。用组织模拟幻影和乳腺活检数据进行的实验表明,所提出的方法在真实的美国图像上效果良好。在相同精度0.4 mm的情况下,与原PIP法相比,速度提高了三倍。通过提前停止优化,可以进一步提高16倍的速度,但代价是准确性的一些损失。
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
EEG source localization by multi-planar analytic sensing 3D general lesion segmentation in CT Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features Iterative nonlinear least squares algorithms for direct reconstruction of parametric images from dynamic PET Pathological image segmentation for neuroblastoma using the GPU
×
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