Zhou Shou-jun, Chen Wu-fan
Key Lab for Medical Image Processing of PLA, Department of Biomedical Engineering, First Military Medical University, Guangzhou 510515, China.
Di Yi Jun Yi Da Xue Xue Bao. 2004 Jun;24(6):677-81.
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.
在医学图像处理研究中，与感兴趣区域相关的运动估计和跟踪受到了相当多的关注。为了提高噪声或杂乱医学图像的质量，基于非线性和非高斯贝叶斯状态估计的粒子滤波器（PF）是一种较好但技术上具有挑战性的解决方案。由于粒子权重算法，特别是重要性密度函数，常常严重影响PF的性能，我们在本文中提出了一种更好的改进算法；此外，为了确保使用PF更好地跟踪动态轮廓，我们提出了一种新的似然和先验概率密度算法。客观的理论评估和大量的对比实验表明，该方法可以成为精确动态轮廓跟踪的良好解决方案。
