DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting.
DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting.
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DOI：
10.1016/j.media.2010.07.002
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发表时间：
2011-08
影响因子：
10.9
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Ou, Yangming;Sotiras, Aristeidis;Paragios, Nikos;Davatzikos, Christos
本文提出了一种通用的变形配准算法，简称为“DRAMMS”。DRAMMS弥补了传统的体素方法和基于地标/特征的方法之间的差距，主要有两个贡献。首先，DRAMMS使每个体素相对独特的可识别的丰富的属性集，从而大大减少匹配的歧义。特别地，提取一组多尺度和多方向的Gabor属性，并选择最佳成分，使得它们形成反映每个体素周围的解剖和几何背景的高度独特的形态特征。此外，构造最佳Gabor属性的方式独立于底层图像模态或内容，这使得DRAMMS通常适用于不同的配准任务。DRAMMS的第二个贡献是，它通过向那些具有更高能力的体素分配更高权重来调制配准，以在图像之间建立唯一的（因此可靠的）对应关系，从而减少那些不太能够找到对应关系的区域的负面影响。一个连续值的加权函数命名为“互显着性”，以反映一对体素之间的匹配可靠性隐含的尝试性变换。因此，体素不像在大多数体素方式的方法中那样同等地贡献，也不像在基于界标/特征的方法中那样孤立地贡献。相反，它们根据连续值互显着图做出贡献，该图在配准过程中动态演变。在模拟图像，受试者间图像，单/多模态图像，从大脑，心脏和前列腺的实验已经证明了DRAMMS的普遍适用性和准确性。
A general-purpose deformable registration algorithm referred to as “DRAMMS” is presented in this paper. DRAMMS bridges the gap between the traditional voxel-wise methods and landmark/feature-based methods with primarily two contributions. First, DRAMMS renders each voxel relatively distinctively identifiable by a rich set of attributes, therefore largely reducing matching ambiguities. In particular, a set of multi-scale and multi-orientation Gabor attributes are extracted and the optimal components are selected, so that they form a highly distinctive morphological signature reflecting the anatomical and geometric context around each voxel. Moreover, the way in which the optimal Gabor attributes are constructed is independent from the underlying image modalities or contents, which renders DRAMMS generally applicable to diverse registration tasks. A second contribution of DRAMMS is that it modulates the registration by assigning higher weights to those voxels having higher ability to establish unique (hence reliable) correspondences across images, therefore reducing the negative impact of those regions that are less capable of finding correspondences. A continuously-valued weighting function named “mutual-saliency” is developed to reflect the matching reliability between a pair of voxels implied by the tentative transformation. As a result, voxels do not contribute equally as in most voxel-wise methods, nor in isolation as in landmark/feature-based methods. Instead, they contribute according to the continuously-valued mutual-saliency map, which dynamically evolves during the registration process. Experiments in simulated images, inter-subject images, single-/multi-modality images, from brain, heart, and prostate have demonstrated the general applicability and the accuracy of DRAMMS.
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