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A Markov random field approach to group-wise registration/mosaicing with application to ultrasound

机译：马尔可夫随机场方法在超声中的分组配准/镶嵌

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摘要

In this paper we present a group-wise non-rigid registration/mosaicing algorithm based on block-matching, which is developed within a probabilistic framework. The discrete form of its energy functional is linked to a Markov Random Field (MRF) containing double and triple cliques, which can be effectively optimized using modern MRF optimization algorithms popular in computer vision. Also, the registration problem is simplified by introducing a mosaicing function which partitions the composite volume into regions filled with data from unique, partially overlapping source volumes. Ultrasound confidence maps are incorporated into the registration framework in order to give accurate results in the presence of image artifacts. The algorithm is initially tested on simulated images where shadows have been generated. Also, validation results for the group-wise registration algorithm using real ultrasound data from an abdominal phantom are presented. Finally, composite obstetrics image volumes are constructed using clinical scans of pregnant subjects, where fetal movement makes registration/mosaicing especially difficult. In addition, results are presented suggesting that a fusion approach to MRF registration can produce accurate displacement fields much faster than standard approaches. (C) 2015 Elsevier B.V. All rights reserved.
机译：在本文中，我们提出了一种基于块匹配的基于分组的非刚性配准/镶嵌算法，该算法是在概率框架内开发的。它的能量功能的离散形式与包含双重和三次团的马尔可夫随机场（MRF）关联，可以使用计算机视觉中流行的现代MRF优化算法对其进行有效地优化。而且，通过引入镶嵌功能将配准体积划分为填充有来自唯一的，部分重叠的源体积的数据的区域，从而简化了配准问题。超声置信度图被合并到配准框架中，以便在存在图像伪影的情况下给出准确的结果。该算法最初在已生成阴影的模拟图像上进行测试。此外，还给出了使用来自腹部体模的真实超声数据进行分组配准算法的验证结果。最后，使用怀孕受试者的临床扫描来构建复合产科图像体积，其中胎儿运动会使配准/镶嵌特别困难。此外，结果表明，MRF配准的融合方法比标准方法可以更快地产生准确的位移场。 （C）2015 Elsevier B.V.保留所有权利。

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