基于增强的粒子滤波算法的医学图像动态轮廓跟踪新方法

关于医学图像的研究,感兴趣区的运动估计和跟踪是一个深受关注的领域。鉴于医学图像质量低、噪声大的普遍特点,从状态变量的非线性、非高斯分布前提出发,利用粒子滤波技术解决该类跟踪问题是一种具有挑战性的技术:由于经典粒子滤波器的权值计算,尤其是重要密度函数的构造方法严重影响了粒子滤波器的性能,本文提出了重要改进。针对用粒子滤波方法估计动态轮廓线这一特殊应用,构造了具有特色的似然和先验概率密度算法。结合客观的理论评价标准和大量比较试验,该方法为精确估计动态轮廓线提供了较好的解决对策。

Dynamic contour tracking of medical images based on improved particle filter

In the research of medical image processing, motion estimation and tracking relating to the region of interest has been given considerable attention. For improving the quality of the noisy or cluttered medical images, the particle filter (PF) based on the non-linear and non-Gaussian Bayesian State Estimation is a better as well as a technically challenging solution. As the algorithm of particle weights, especially the importance density function, often severely affects the performance of the PF, we propose in this paper a better algorithm for its improvement; in addition, to ensure better tracking of the dynamic contour with the PF, we proposed a new algorithm for the likelihood and prior probability density. Objective theoretical evaluation and substantial comparative experiments suggest that this method can be a good solution for accurate dynamic contour tracking.