基于灰度距离融合的2D/3D刚性配准

王雷; 高欣; 崔学理; 梁志远

doi:10.3788/OPE.20142210.2815

您当前的位置：

首页 >

文章列表页 >

基于灰度距离融合的2D/3D刚性配准

信息科学 | 更新时间：2020-08-13

- 基于灰度距离融合的2D/3D刚性配准
- 2D/3D rigid registration by integrating intensity distance
- 光学精密工程 2014年22卷第10期页码：2815-2824
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
  3. 中国科学院苏州生物医学工程技术研究所,江苏苏州,中国,215163
  4. 首都医科大学生物医学工程学院北京,100069
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.81371640)();苏州市国际合作项目(No.SH201210)();江苏省临床医药专项资金资助项目(No.BL2012049)()
- DOI：10.3788/OPE.20142210.2815
  中图分类号： R616;TP391
- 收稿日期：2014-03-24，
  
  修回日期：2014-04-29，
  
  纸质出版日期：2014-10-25
- 稿件说明：
移动端阅览
王雷, 高欣, 崔学理等. 基于灰度距离融合的2D/3D刚性配准[J]. 光学精密工程, 2014,22(10): 2815-2824

WANG Lei, GAO Xin, CUI Xue-li etc. 2D/3D rigid registration by integrating intensity distance[J]. Editorial Office of Optics and Precision Engineering, 2014,22(10): 2815-2824
王雷, 高欣, 崔学理等. 基于灰度距离融合的2D/3D刚性配准[J]. 光学精密工程, 2014,22(10): 2815-2824 DOI： 10.3788/OPE.20142210.2815.

WANG Lei, GAO Xin, CUI Xue-li etc. 2D/3D rigid registration by integrating intensity distance[J]. Editorial Office of Optics and Precision Engineering, 2014,22(10): 2815-2824 DOI： 10.3788/OPE.20142210.2815.

摘要

针对影像导航手术提出了一种基于灰度距离融合的2D/3D刚性图像配准方法.该方法使用一种新的灰度距离信息对最常使用的传统相似性测度(互信息

互相关及模式强度)进行约束

构建一类新的相似性测度(距离互信息

距离互相关和距离模式强度)

并用维也纳医科大学公开发表的2D/3D刚性配准金标准数据来评估新测度的配准性能.与基于灰度的传统相似性测度相比

文中方法构造的新相似性测度在平均目标配准误差(mTRE)的均值和标准差上均有显著降低

其中均值至少降低了28.15%

标准差最少降低了61.17%.以mTRE小于2 mm为配准成功的依据时

新测度的配准成功率比传统测度至少提高了25.56%.此外

新测度在配准优化过程中的迭代次数比传统测度平均降低了35.59%.结果显示:基于灰度距离融合的2D/3D刚性配准方法比基于单一灰度的配准方法具有更好的图像配准性能.

Abstract

For image-guided surgery

a novel 2D/3D image rigid registration method is proposed by integrating intensity distances of images. The method uses a new intensity distance information to restrict the most commonly used similarity measures(Mutual Information (MI)

Cross Correlation (CC) and Pattern Intensity (PI)) and to construct a kind of novel similarity measures(distance MI

distance CC and distance PI). These novel measures are evaluated by using the porcine skull phantom datasets from the Medical University of Vienna. The experiments show that novel measures are better than traditional measures

i.e.

the mean and standard deviation of mean Target Registration Errors (mTRE) by novel measures are respectively lower by at least 28.15% and 61.17% than those by traditional measures. When setting mTRE less than 2 mm as successful registration

the success rate with novel measures increases by at least 25.56% on average. Meanwhile

the average iteration times of novel measures also reduce by 35.59% than those of traditional measures. This results suggest that the novel registration method using novel measures has better performance of registration than intensity-based methods using traditional measures in terms of the accuracy and robustness for 2D/3D rigid registration.

关键词

Keywords

references

MARKELJ P, TOMAZEVIC D, LIKAR B, et al.. A review of 3D/2D registration methods for image-guided interventions [J]. Medical Image Analysis, 2012, 16: 642-661.

BIRKFELLNER W, STOCK M, FIGL M, et al.. Stochastic rank correlation: A robust merit function for 2D/3D registration of image data obtained at different energies [J]. Medical Physics, 2009, 36: 3420-3432.

HIPWELL J H, PENNEY G P, MCLAUGHLIN R A, et al.. Intensity-based 2D/3D registration of cerebral angiograms [J]. IEEE Transactions on Medical Imaging, 2003, 22:1417-1426.

PENNEY G P, WEESE J, LITTLE J A, et al.. A comparison of similarity measures for use in 2D/3D medical image registration [J]. IEEE Transactions on Medical Imaging, 1998, 17:586-595.

余先川, 吕中华, 胡丹. 遥感图像配准技术综述 [J]. 光学精密工程, 2013,21(11): 2960-2972. YU X CH, LV ZH H, HU D. Review of remote sensing image registration techniques [J]. Opt. Precision Eng., 2013, 21(11): 2960-2972.(in Chinese)

纪华. 结合全局信息的SIFT特征匹配算法 [J]. 光学精密工程 2009, 17(2):439-444. JI H. SIFT features matching algorithm with global information [J]. Opt. Precision Eng., 2009, 17(2):439-444.(in Chinese)

MARKELJ P, TOMAZEVIC D, PERNUS F, et al.. Robust gradient-based 3-D/2-D registration of CT and MR to X-ray images [J]. IEEE Transactions on Medical Imaging, 2008, 27:1704-1714.

MYRONENKO A, SONG X. Point set registration: Coherent point drift [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32:2262-2275.

FITZGIBBON A W. Robust registration of 2D and 3D point sets [J]. Image and Vision Computing, 2003, 21:1145-1153.

TOMAZEVIC D, LIKAR B, SLIVNIK T, et al.. 3-D/2-D registration of CT and MR to X-ray images [J]. IEEE Transactions on Medical Imaging, 2003, 22:1407-1416.

刘博超, 赵建, 孙强. 基于边缘改进的Harris角点检测方法 [J]. 液晶与显示 2013, 28(6) 939-942. LIU B CH, ZHAO J, SUN Q. Improved Harris corner detection method based on edge [J]. Chinese Journal of Liquid Crystals and Displays, 2013, 28(6):939-942.(in Chinese)

苏可心, 韩广良, 孙海江. 基于SURF的抗视角变换图像匹配算法 [J]. 液晶与显示 2013, 28(4) 626-632. SU K X, HAN G L,SUN H J. Anti-viewpoint changing image matching algorithm based on SURF [J]. Chinese Journal of Liquid Crystals and Displays, 2013, 28(4) 626-632.(in Chinese)

PAWIRO S, MARKELJ P, PERNUS F, et al.. Validation for 2D/3D registration I: A new gold standard data set [J]. Medical Physics, 2011, 38:1481-1502.

KRAATS E B, PENNEY G P, TOMAZEVIC D, et al.. Standardized evaluation methodology for 2-D-3-D registration [J]. IEEE Transactions on Medical Imaging, 2005, 24:1177-1189.

GENDRIN C, MARKELJ P, PAWIRO S A, et al.. Validation for 2D/3D registration II: The comparison of intensity-and gradient-based merit functions using a new gold standard data set [J]. Medical Physics, 2011, 38:1491-1505.

RUSSAKOFF D B, ROHLFING T, ADLER J R, et al.. Intensity-based 2D-3D spine image registration incorporating a single fiducial marker [J]. Academic Radiology, 2005, 12:37-50.

KNZLER T, GREZDO J, BOGNER J, et al.. Registration of DRRs and portal images for verification of stereotactic body radiotherapy: a feasibility study in lung cancer treatment [J]. Physics in Medicine and Biology, 2007, 52:2157-2164.

SPOERK J, BERGMANN H, WANSCHITZ F, et al.. Fast DRR splat rendering using common consumer graphics hardware [J]. Medical Physics, 2007, 34:4302-4307.

BIRKFELLNER W, SEEMANN R, FIGL M, et al.. Wobbled splattinga fast perspective volume rendering method for simulation of X-ray images from CT [J]. Physics in Medicine and Biology, 2005, 50:73-78.

PLUIM J P, MAINTZ J A, VIERGEVER M A. Mutual-information-based registration of medical images: a survey [J]. IEEE Transactions on Medical Imaging, 2003, 22:986-1004.

FITCHARD E, ALDRIDGE J, RECKWERDT P, et al.. Registration of synthetic tomographic projection data sets using cross-correlation [J]. Physics in Medicine and Biology, 1998, 43:1645, 1998.

CAMP J, ROBB R A. Novel binning method for improved accuracy and speed of volume image co-registration using normalized mutual information [J]. Medical Imaging, 1999:24-31.

WANG L, GAO X, FANG Q. A novel mutual information-based similarity measure for 2D/3D registration in image guided intervention[C]. 2013 IEEE International Conference on Orange Technologies,2013:135-138.

SCHROEDER W, NG L, AND CATES J. The itk Software Guide 2.4 [M]. USA: Kitware Inc, 2005:36-41.

GENDRIN C, SPOERK J, BLOCH C, et al.. Towards real-time 2D/3D registration for organ motion monitoring in image-guided radiation therapy[C]. SPIE in Medical Imaging, San Diego, USA, 2010: 762512-762512-9.

BERNON J, BOUDOUSQ V, ROHMER J, et al.. A comparative study of Powell's and Downhill Simplex algorithms for a fast multimodal surface matching in brain imaging [J]. Computerized Medical Imaging and Graphics, 2001, 25:287-297.

TURGEON G A, LEHMANN G, GUIRAUDON G, et al.. 2D-3D registration of coronary angiograms for cardiac procedure planning and guidance [J]. Medical Physics, 2005, 32:3737-3741.

浏览量

506

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据