Motion prediction via online instantaneous frequency estimation for vision-based beating heart tracking
Motion prediction via online instantaneous frequency estimation for vision-based beating heart tracking
复制标题
通过在线瞬时频率估计进行运动预测，用于基于视觉的心跳跟踪
DOI：
10.1016/j.inffus.2016.09.004
复制
发表时间：
2017-05-01
影响因子：
18.6
通讯作者：
Liu, Shan
中科院分区：
文献类型：
--
作者：
Yang, Bo;Liu, Chao;Liu, Shan
在微创手术中，由于场景动态变化大，成像条件差，使得基于立体内窥镜的心脏跳动轨迹跟踪具有挑战性。本文提出了一种新的预测方法，用于心脏运动的鲁棒跟踪。双时变傅立叶级数用于对心脏表面上的感兴趣点（POI）的运动进行建模，该运动由呼吸和心跳运动共同驱动。采用双卡尔曼滤波方法分别估计模型的频率和傅立叶系数。提出了一种新的正交分解算法，用于从POI的三维轨迹中在线测量呼吸和心跳运动的瞬时频率。通过对过去轨迹进行主成分分析，利用呼吸和心跳运动之间的方向差异，提取最佳1D主成分信号用于测量相应的频率。基于加性噪声模型融合从正交子带计算的频率以用于最佳频率测量。所提出的方法进行了评估，并与其他可用的预测方法的基础上的模拟数据和实际测量的信号，从视频记录的达芬奇（R）手术机器人。预测算法最终被纳入一个完善的视觉跟踪方法来处理长期的闭塞。(C)2016爱思唯尔B.V.保留所有权利。
The beating heart tracking based on stereo endoscope remains challenging due to highly dynamic scenes and poor imaging conditions in minimally invasive surgery. This paper proposes a new prediction method for robust tracking of heart motion. The dual time-varying Fourier series is used for modeling the motion of points of interest (POI) on heart surfaces, which is driven jointly by breathing and heartbeat motion. A dual Kalman filtering scheme is used to estimate the frequencies and Fourier coefficients of the model respectively. A novel orthogonal decomposition algorithm is developed to measure the instantaneous frequencies of breathing and heartbeat motion online from the 3D trajectory of the POI. The difference in direction between breathing and heartbeat motion is exploited by using principal component analysis on the past trajectory, and optimal 1D principal component signals are extracted for measuring the corresponding frequencies. The frequencies calculated from the orthogonal subbands are fused based on an additive noise model for optimal frequency measurement. The proposed method is evaluated and compared with other available prediction methods based on the simulated data and the real-measured signals from the videos recorded by the daVinci (R) surgical robot. The prediction algorithm is finally incorporated into a well-established visual tracking method to handle long-term occlusions. (C) 2016 Elsevier B.V. All rights reserved.