摘要
针对传统个性化肝脏模型存在的效率、精度等方面的问题,提出一种高精度肝脏模型的个性化数据驱动方法。对先前研究得到的高精度肝脏模型以及个性化肝脏CT数据分别进行加工处理,选取一些特征点;用PCA算法实现初始配准、ICP算法实现特征点的配准并以初步配准得到的初始位置为基础用改进的ICP算法计算得到两个点集之间的稠密对应关系;利用点集之间的对应关系引导肝脏模型进行RBF形变,实现肝脏模型的外表面个性化建模。实验结果表明,算法在保证模型的个性化和高精度的情况下具有一定的时间效率,可以得到理想的三维模型。
The personalization of 3D liver models and the acquisition of liver models with high precision are two key technologies in virtual surgery systems. A personalized data-driven method aiming at the problems of time efficiency and precision of liver models was proposed. The liver model with high precision from previous research achievements and personal CT datasets were processed respectively, and then some feature points were marked on both the model and the CT datasets. The PCA algorithm was used to get the initial matching, the ICP algorithm was used to get the matching of the feature points, and then an improved ICP algorithm was used to calculate and get the dense correspondence of two datasets based on the matching results of previous matching algorithms. The dense correspondence of the two datasets was used to lead the liver model to perform the RBF deformation to finally acquire the personalized 3D liver models. The results show that the algorithm is time-efficient and can get ideal 3D liver models using this algorithm.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2015年第2期270-278,共9页
Journal of System Simulation
基金
福建省科技计划重点项目(2011H0027)
福建省自然科学基金(2013J05090)
关键词
肝脏模型
个性化
高精度
模型驱动
liver model
personalized
high precision
data-driven
