摘要
针对计算机辅助手术三维导航技术中术前CT图像与术中实际空间的配准问题,提出一种基于最近点迭代(ICP,Iterative Closest Point)算法的特征点云配准技术.利用医学图像空间和实际空间特征区域的两片点云坐标进行三维配准.对CT图像进行重建、分割及交互式操作得到医学图像特征点云;利用光学定位仪实时采集实际空间中对应区域的点云;通过主元分析(PCA,Principal Component Analysis)获取两组点云数据的特征向量进行初配准;进行最近点迭代使配准矩阵收敛到一个最优解,其中采用k-d tree寻找邻近点加速迭代过程.以塑料脊柱模型骨为对象进行了脊柱手术导航配准精度实验,进一步对实验中的点云数据加入高斯噪声以进行误差分析.结果表明这种配准方法简单可靠,在模型骨情况下配准精度在1 mm以内.
Registration between medical images and physical space is an important procedure in 3D surgical navigation systems. A registration method based on ICP （iterative closest point） algorithm was presented. Two point sets, one of which is acquired from physical space by the optical localizer and the other is from a 3D model reconstructed by the marching cube method, were used to calculate the transformation matrix between preoperative CT image space and intraoperative fiducial marker space. The registration procedure was divided into the following two phases. PCA （principal component analysis） was used to calculate the eigenvectors of the two point sets respectively to achieve initial registration. ICP method was used to make the initial transfor- mation matrix to converge into the best solution, in which the k-d tree structure was used to accelerate the procedure. A spine model experiment was carried out and a virtual 3D measurement environment was set up to evaluate the accuracy of the registration method. Error analysis was conducted by adding Gaussian noise to the point sets. The result shows that under this condition the final average registration accuracy is less than 1 mm.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
2009年第4期434-438,共5页
Journal of Beijing University of Aeronautics and Astronautics
基金
国家杰出青年科学基金资助项目(60525314)
国家科技支撑计划资助项目(2006BAI03A16)
北京市科技计划资助项目(D020602500007093)
武警北京总队第二医院资助项目(BUAA20080104)
关键词
图像配准
最近点迭代
手术导航
三维重建
轮廓提取
image registration
ICP （ iterative closest point）
surgical navigation
3D reconstruction
contour extraction
