基于光流和多层次B样条自由变形的医学图像鲁棒形变配准  

Robust Deformable Medical Image Registration Using Optical Flow and Multilevel Free Form Deformation

作　　者：王敏尤[1] 胡海波[1] 秦斌杰[1] 

机构地区：[1]上海交通大学生命科学技术学院,上海200240

出　　处：《上海交通大学学报》2008年第10期1660-1664,共5页Journal of Shanghai Jiaotong University

基　　金：国家自然科学基金资助项目(60402021);上海市科委基础研究重点项目(04JC14060)

摘　　要：为了跟踪肿瘤病灶对周围正常组织挤压变形,以及脑部手术软组织变形对手术的影响,提出了光流估计框架下的多层次B样条自由变形鲁棒形变配准方法.利用光流法约束下的多层次B样条自由变形,通过加密局部控制点以加强局部形变,能有效建模肿瘤信号及异常变形区域的局部较大形变.通过引入鲁棒估计,给予异常信号(病灶、灰度突变或形变不连续区域)以较小的权值,可减小图像灰度差异、局部不连续形变场所造成异常信号对形变配准优化的不利影响.采用了L-BFGS方法,可减少内存开销,提高优化速度.实验表明:形变配准算法得到了较好的效果,可较好建模术前和术中脑部医学图像的形变,本配准算法可用于指导图像导向手术.In order to track the brain tissue shift due to tumor resection during image guided surgery, this paper presented a framework for robust deformable registration based on the theory of optical flow, within which outlier detection is used to set up locally-refined control points in multilevel free form deformation （FF＇D） model. Robust M-estimator was also introduced to weaken the effect of outliers in global optimization for getting improved registration result. This proposed method has successfully been applied to a number of registration tasks to demonstrate its applicability and robustness to outliers.

关 键 词：异常信号 形变配准 光流法 自由形变模型 鲁棒估计 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] R318[自动化与计算机技术—计算机科学与技术]