医学脊椎图像的配准研究
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摘要
计算机辅助脊柱手术导航是近年来在国际上发展迅速的一个领域,它对于确定病灶位置、制定最佳手术方案和保证手术成功等都具有十分重要的意义。图像配准是整个导航技术的关键。
本文首先介绍了课题研究的意义、医学图像配准的发展,然后对图像配准方法进行分类,并总结了典型的配准方法。其次深入研究基于互信息的医学图像配准方法,并探讨互信息的相关理论和图像配准框架。提出在多分辨率策略下,以Mattes互信息法作为相似性测度,对图像进行刚性变换,1+1演变法进行优化的新配准算法。并对几种不同的基于互信息的方法进行了实验,对比配准结果发现新方法速度更快、精确度更高。
在详细分析脊柱解剖结构和实际情况基础上,本文提出了一种新的基于脊柱图谱的分步配准算法。该方法首先对图谱和MR图像进行预处理,利用仿射变换实现全局初配准,然后对椎骨实行分段刚性配准、对脊柱周围的组织作弹性配准,接着对器官作位置配准,最后利用优先级判据组合各配准结果。实验表明该方法配准精度高、稳定性好。
In recent years, Computer-aided spine surgery navigation is an area that has been developing very quickly in the intentional. It is of great significance to determine lesion location, develop the best surgical program and ensure successful operations. Image registration is the key of the entire navigation.
Firstly, the paper generally introduces the development and significance of medical registration techniques, and then categorizes them. The typical methods are also summarized. Secondly, the mutual information-based medical image registration methods are deeply researched, and the relevant theory and framework of mutual information registration is discussed. Then, the paper proposes a mixed optimization algorithm in image registration which takes Mattes mutual information as similar measure under the multi-resolution strategy, and changes with rigid transformation, researches with 1+1 evolution strategy. Besides, the paper carries on some experiments based on registration of mutual information. Experiments show that t speed of the new method is quicker, the precision to be higher.
After mostly study of spinal anatomy, relevant organizations and integration of the actual situation, the paper presents a sub-step registration algorithm based on spinal atlas. The new methods preprocess Atlas and MR image at first, and then the global registration for Atlas and MR image , rigid transformation for sub-vertebral; elastic registration for tissue around the spine; location registration for organs. Experiments show that the new method with high precision and strong stablity.
本文首先介绍了课题研究的意义、医学图像配准的发展,然后对图像配准方法进行分类,并总结了典型的配准方法。其次深入研究基于互信息的医学图像配准方法,并探讨互信息的相关理论和图像配准框架。提出在多分辨率策略下,以Mattes互信息法作为相似性测度,对图像进行刚性变换,1+1演变法进行优化的新配准算法。并对几种不同的基于互信息的方法进行了实验,对比配准结果发现新方法速度更快、精确度更高。
在详细分析脊柱解剖结构和实际情况基础上,本文提出了一种新的基于脊柱图谱的分步配准算法。该方法首先对图谱和MR图像进行预处理,利用仿射变换实现全局初配准,然后对椎骨实行分段刚性配准、对脊柱周围的组织作弹性配准,接着对器官作位置配准,最后利用优先级判据组合各配准结果。实验表明该方法配准精度高、稳定性好。
In recent years, Computer-aided spine surgery navigation is an area that has been developing very quickly in the intentional. It is of great significance to determine lesion location, develop the best surgical program and ensure successful operations. Image registration is the key of the entire navigation.
Firstly, the paper generally introduces the development and significance of medical registration techniques, and then categorizes them. The typical methods are also summarized. Secondly, the mutual information-based medical image registration methods are deeply researched, and the relevant theory and framework of mutual information registration is discussed. Then, the paper proposes a mixed optimization algorithm in image registration which takes Mattes mutual information as similar measure under the multi-resolution strategy, and changes with rigid transformation, researches with 1+1 evolution strategy. Besides, the paper carries on some experiments based on registration of mutual information. Experiments show that t speed of the new method is quicker, the precision to be higher.
After mostly study of spinal anatomy, relevant organizations and integration of the actual situation, the paper presents a sub-step registration algorithm based on spinal atlas. The new methods preprocess Atlas and MR image at first, and then the global registration for Atlas and MR image , rigid transformation for sub-vertebral; elastic registration for tissue around the spine; location registration for organs. Experiments show that the new method with high precision and strong stablity.
引文
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[4l]裴巍.神经外科脑解剖图谱的三维可视化应用[D],上海交通大学,2008.1.
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[2]Woods R P,Mazziotta J C,Cherry S R.MRI-PET registration with automated algorithm[J].Journal of Computer Assisted Tomography.1993,17(4):536-546.
[3]J.Hajnal,D.J.Hawkes,D.Hill.Medical Image Registration[M].CRC Press 2001.
[4]R Bajcsy,S Kovagie.Multiresolution elastic matching[J].Computer vision,Graphics an Image Processing,1989,46:1-21.
[5]Christensen G E,Rabbit R D,Miller M I,et al.Deformable templates using large deformation Kinematics[J].IEEE Trans on Image Processing,1996,5(10):1435-1447.
[6]B.Zitova,J.FIusser.Image Registration Method:A Survey[J].Image and Vision Computing,2003,21(11):977-1000
[7]J B Antoine Maintz,M A Viergever.A survey of medical image registration[J].Medical Image Analysis,1998,2(1):1-36.
[8]陶观群,李大鹏,陆光华.基于球形标记的多模态医学体配准[J].国外医学生物医学工程分册,2003,26(1):16-19.
[9]黄永锋,李杉,赵俊.基于外标记点的2D/3D图像配准方法[J].上海交通大学学报,2006,40(7):1108-1111.
[10]Karin Hensel,Stephanie Hold,Susanne Winter et al.Ultrasound Based Navigation System for Minimal Invasive Surgery at the Lumbar Spine within OrthoMIT[J].Advances In Medical Engineering,Springer,2007.
[11]Ashok Panigrahy,Shelton D.Caruthers,Registration of Three-Dimensional MR and CT Studies of the Cervical Spine[J].AJNR Am.J.Neuroradiol,2000,21(2):282-289.
[12]贾富仓,李修往,陈思平.CT图像引导脊柱外科配准方法研究[J]I深圳大学学报(理工版,2007,24(4):406-409.
[13]张翼,王满宁,宋志坚.脊柱手术导航中分步式2D/3D图像配准方法[J].计算机辅助设计与图形学学报,2007,19(9):1154-1165.
[14]吴锋,钱宗才,杭洽时等.基于轮廓的力矩主轴法在医学图像配准中的应用[J].第四军医大学学报,2001,22f6):567-569.
[15]李晓丽.医学图像分割与配准研究[D].山东大学,2007.4.
[16]韦春荣,张孝飞,陈洪波,等.基于轮廓提取的医学图像配准方法[J].广西师范大学学报(自然科学版),2003,21(2):33-36.
[17]俎栋林,余立锋,王卫东.基于轮廓特征的多模态医学图像的配准[J].系统工程与电子技,2000,22(7):61-64.
[18]火元莲,齐永锋,宋海声.基于轮廓特征点最大互信息的多模态医学图像配准[J].激光与红外,2008,38(1):96-98.
[19]周永新,罗述谦.基于形状特征点最大互信息的医学图像配准[J].计算机辅助设计与图 形学学报,2002,14(7):654-658.
[20]程孝国.基于3D轮廓表面的医学图像配准与融合方法研究[D].山东大学,2006.4.
[21]Guezier A,Kazanzides P,Williamson B,et al.Anatomy-based registration of CT-scan and intraoperative X-rays images for guiding a surgical robot[J].IEEE Trans Med Imaging,1998,17(5):715-728.
[22]Balter J,Pelizzari C,Chen G.Correlation of projection radiographies in radiation therapy using open curve segments and points[J].Med Phys,1992,19:329-334.
[23]Gunilla Borgefors.Distance Transformations in Digital Images[J].Computer Vision,Graphics,and image Processing,1986,34:344-3711.
[24]J.A.Little,D.L.G.Hill,D.J.Hawkes.Deformations Incorporating Rigid Structures[J],Computer Vision And Image Understanding,1997,66:223-232.
[25]卢振泰.医学图像配准算法研究[D],南方医科大学,2008.4.
[26]何力,周康源,李长富.基于流体映射模型的医学图像弹性配准[J].北京生物医学工程,2005,24(5):366-369.
[27]龚永义,罗笑南,贾维嘉.基于改进的弹簧质子模型的医学图像配准[J].计算机学报,2008,31(7):1224-1233.
[28]李翔.基于统计模型的多模态医学图像配准[D].浙江大学,2002.5.
[29]刘新刚,陈武凡,陈光杰.基于先验知识和MRF随机场模型的医学图像弹性配准方法[J].中国生物医学工程学报,2006,25(2):151-157.
[30]Kass M,Terzopoullos W A.Snakes:Active Contour Models[J].International Journal of Computer Vision,1988,1(4):321-331.
[31]Yongmei Wang,Lawrence H.Staib.Physical model-based non-rigid registration incorporating statistical shape information[J].Medical Image Analysis,2000,20(7):7-20.
[32]Said Benameur,Max Mignotte,Hubert Labelle etal.A Hierarchical Statistical Modeling Approach for the Unsupervised 3-D Biplanar Reconstruction of the Scoliotic Spine[J].IEEE Trans.Biomed.Eng,2005,52(12):2041-2057.
[33]S.Benameur,M.Mignotte,S.Parent etal.3D/2D registration and segmentation of scoliotic vertebrae using statistical models[J].Computerized Med.Imag.Graphics,2003,27(5):321-337.
[34]Wenjun Li,Miki Sode,Isra Saeed.Automated registration of hip and spine for longitudinal QCT studies:Integration with 3D densitometric and structural analysis[J].Bone,2006,38(2):273-279.
[35]Samuel Kadoury,Nikos Paragios.3D Registration of Articulated Spine Models Using Markov Random Fields[R].Institute national de recherche en information et en automotive,2009.4.
[36]赵增军,李宝生.基于模型的医学图像形变配准在图像引导放疗中的应用研究进展[J].中国图象图形学报,2009,14(8):1504-1509.
[37]李博文.三维数字化人脑图谱的构建及其应用[D].上海交通大学,2008.1.
[38]罗述谦,阎华.基于薄板样条的MRI图像与脑图谱的配准方法[J].中国生物医学工程 学报,2004,23(6):479-485.
[39]邹晖.基于标准化图谱的数字化三维人脑图谱研究[D].东南大学硕士论文,2004.6.
[40]肖化,戴飞鹏,陈小兰.基于Talairach坐标系统的脑图谱配准算法的实现[J].华南师范大学学报(自然科学版),2008,1:40-45.
[4l]裴巍.神经外科脑解剖图谱的三维可视化应用[D],上海交通大学,2008.1.
[42]G.Subsol,J.Ph.Thirion,N.Ayache.Application of an automatically built 3D morphometric brain atlas:Study of cerebral ventricle shape[J].Visualization in Biomedical Computing,1996,1131:373-382.
[43]Valerie Duay,Xavier Bresson,F.Javier Sanchez Castro,et al.An Active Contour-Based Atlas Registration Model Applied to Automatic Sub thalamic Nucleus Targeting on MRI:Method and Validation[C].MICCAI,2008,(2):980-988.
[44]Meritxell Bach Cuadra,Mathieu De Craene,Valerie Duay,et al.Dense deformation field estimation for atlas-based segmentation of pathological MR brain images[C].Computer Methods and Programs in Biomedicine,2006,84(23):66-75.
[45]Mei Chen.3D Deformable Registration Using a Statistical Atlas with Applications in Medicine[D].Carnegie Mellon University,1999.10.
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