心脏序列图像运动估计新方法:基于广义模糊梯度矢量流场的形变曲线运动估计与跟踪

应用动态轮廓线模型(ACM)解决心脏运动估计问题是该领域的主要研究方法之一．采用经典外力和传统ACM模型对感兴趣边缘进行搜索及跟踪时，普遍存在模型的局部适应性程度不高的缺陷．为解决这一挑战性难题，该文提出了广义模糊梯度矢量流(GFGVF)的概念，并构造出一组新的Snake平衡方程，该方程可对心脏内部边缘逐帧进行鲁棒跟踪．为进一步跟踪每一特征点的运动，该文将前一步的轮廓跟踪结果作为似然条件，结合一致性和连续性先验条件，通过最大后验概率(MAP)的方法对整个过程进行了优化计算．通过对MR及CT两类心脏序列图像进行运动跟踪实验并对计算结果进行多种比较，此方法显示了较好的鲁棒性．

A New Approach to the Motion Estimation of Cardiac Image Sequences: Active Contours Motion Tracking Based on the Generalized Fuzzy Gradient Vector Flow

Using the active contours model (ACM) to estimate the cardiac motion, the new concept of generalized fuzzy gradient vector flow (GFGVF) is presented in this paper. The GFGVF is refered as a component of external force and associated with optical flow field (OFF) to build a set of Snake equations. After the GFGVF and OFF are accurately calculated respectively, the initial outline can gradually approach to the region of interest (ROI) edges in the images under the constraint of Snake equations and track the ROI from frame to frame. Under some constrained conditions, the motion states of some feature points in the edge of ROI can be found by the Maximum a Posteriori Probability (MAP) during a period of cardiac motion. Then, the motion estimation is well optimized. Another, the coefficients in the set of ACM equations can be found using the prior information, which avoids giving them by experience and improves the capability of edge tracing of ROI. By a period of motion tracking for CT and MR cardiac image sequences, the experiments show that the method can robustly simulate the motion of the cardiac left ventricle (LV) and left atria (LA) , moreover, the simulation result of them using GFGVF is obviously better than the one using GVF.