A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.
A fast hybrid algorithm combining regularized motion tracking and predictive search for reducing the occurrence of large displacement errors.
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DOI：
10.1109/tuffc.2011.1865
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发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
Hall TJ
中科院分区：
文献类型：
--
作者：
Jiang J;Hall TJ
一个混合的方法，继承了正则化运动跟踪方法的鲁棒性和预测搜索方法的效率。其基本思想是使用正则化斑点跟踪，以获得高质量的种子，在探索性搜索，可用于后续的智能预测搜索。的混合散斑跟踪算法的性能进行了比较与三个已发表的散斑跟踪方法，使用在体内乳腺病变数据。我们发现，混合算法提供了更高的位移质量度量值，较低的均方根误差相比，局部平滑的位移场，和更高的改善率相比，经典的块匹配算法。在这些比较的基础上，我们得出结论，混合方法可以进一步提高斑点跟踪的准确性相比，其实时同行，在略高的计算需求为代价。
A hybrid approach that inherits both the robustness of the regularized motion tracking approach and the efficiency of the predictive search approach is reported. The basic idea is to use regularized speckle tracking to obtain high quality seeds in an explorative search that can be used in the subsequent intelligent predictive search. The performance of the hybrid speckle tracking algorithm was compared with three published speckle tracking methods using in vivo breast lesion data. We found that the hybrid algorithm provided higher displacement quality metric values, lower root mean squared errors compared to a locally smoothed displacement field, and higher improvement ratios compared to the classic block-matching algorithm. On the basis of these comparisons, we concluded that the hybrid method can further enhance the accuracy of speckle tracking compared to its real-time counterparts, at the expense of slightly higher computational demand.