Distance Transforms in Multi Channel MR Image Registration.

Distance Transforms in Multi Channel MR Image Registration.
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多通道 MR 图像配准中的距离变换。

DOI:
10.1117/12.878367
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发表时间:
2011
期刊:
Proceedings of SPIE--the International Society for Optical Engineering
影响因子:
--
通讯作者:
Prince,JerryL
Prince,JerryL
中科院分区:
--
文献类型:
--
作者:
Chen,Min;Carass,Aaron;Bogovic,John;Bazin,Pierre-Louis;Prince,JerryL

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可变形配准技术在各种医学成像任务中扮演着重要的角色,如图像融合、分割和术后评估。近年来,互信息已成为医学图像配准算法中应用最广泛的相似性度量之一。不幸的是,作为一种匹配标准,当统计一致性较差和缺乏结构时,互信息就失去了很大的有效性。在强度均匀且信息稀疏的图像区域中尤其如此。在这里,我们提出了一种方法,旨在通过将解剖分段的距离变换作为配准算法中多通道互信息框架的一部分来解决这一问题。我们的方法通过配准真实的磁共振脑数据并将结果的分割结果与目标的分割结果进行比较来测试。我们的分析表明,通过将白质分割的距离变换结合到配准中,配准结果的整体分割比不使用距离变换时更接近目标。
Deformable registration techniques play vital roles in a variety of medical imaging tasks such as image fusion, segmentation, and post-operative surgery assessment. In recent years, mutual information has become one of the most widely used similarity metrics for medical image registration algorithms. Unfortunately, as a matching criteria, mutual information loses much of its effectiveness when there is poor statistical consistency and a lack of structure. This is especially true in areas of images where the intensity is homogeneous and information is sparse. Here we present a method designed to address this problem by integrating distance transforms of anatomical segmentations as part of a multi-channel mutual information framework within the registration algorithm. Our method was tested by registering real MR brain data and comparing the segmentation of the results against that of the target. Our analysis showed that by integrating distance transforms of the the white matter segmentation into the registration, the overall segmentation of the registration result was closer to the target than when the distance transform was not used.
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